- See Also
-
Links
- “Does Progress on ImageNet Transfer to Real-world Datasets?”, Et Al 2023
- “Pruning Compact ConvNets for Efficient Inference”, Et Al 2023
- “EarSpy: Spying Caller Speech and Identity through Tiny Vibrations of Smartphone Ear Speakers”, Et Al 2022
- “What Do Vision Transformers Learn? A Visual Exploration”, Et Al 2022
- “Simulated Automated Facial Recognition Systems As Decision-aids in Forensic Face Matching Tasks”, 2022
- “Predicting Sex, Age, General Cognition and Mental Health With Machine Learning on Brain Structural Connectomes”, Et Al 2022
- “Interpreting Neural Networks through the Polytope Lens”, Et Al 2022
- “The Power of Ensembles for Active Learning in Image Classification”, Et Al 2022
- “GCN-Based Multi-Modal Multi-Label Attribute Classification in Anime Illustration Using Domain-Specific Semantic Features”, Et Al 2022
- “The Unreasonable Effectiveness of Fully-Connected Layers for Low-Data Regimes”, Et Al 2022
- “Understanding the Covariance Structure of Convolutional Filters”, Et Al 2022
- “VICRegL: Self-Supervised Learning of Local Visual Features”, Et Al 2022
- “FastSiam: Resource-Efficient Self-supervised Learning on a Single GPU”, Et Al 2022
- “Evaluation of Transfer Learning Methods for Detecting Alzheimer’s Disease With Brain MRI”, Et Al 2022
- “Reassessing Hierarchical Correspondences between Brain and Deep Networks through Direct Interface”, 2022
- “Timesweeper: Accurately Identifying Selective Sweeps Using Population Genomic Time Series”, 2022
- “RHO-LOSS: Prioritized Training on Points That Are Learnable, Worth Learning, and Not Yet Learnt”, Et Al 2022
- “BigVGAN: A Universal Neural Vocoder With Large-Scale Training”, Et Al 2022
- “Continual Pre-Training Mitigates Forgetting in Language and Vision”, Et Al 2022
- “Scaling Up Your Kernels to 31×31: Revisiting Large Kernel Design in CNNs (RepLKNet)”, Et Al 2022
- “Variational Autoencoders Without the Variation”, Et Al 2022
- “On the Effectiveness of Dataset Watermarking in Adversarial Settings”, 2022
- “Approximating CNNs With Bag-of-local-Features Models Works Surprisingly Well on ImageNet”, Et Al 2022
- “ConvMixer: Patches Are All You Need?”, 2022
- “HyperTransformer: Model Generation for Supervised and Semi-Supervised Few-Shot Learning”, Et Al 2022
- “ConvNeXt: A ConvNet for the 2020s”, Et Al 2022
- “An Empirical Investigation of the Role of Pre-training in Lifelong Learning”, Et Al 2021
- “Noether Networks: Meta-Learning Useful Conserved Quantities”, Et Al 2021
- “The Efficiency Misnomer”, Et Al 2021
- “Evaluating Loss Functions for Illustration Super-Resolution Neural Networks”, 2021
- “Deep Learning Models of Cognitive Processes Constrained by Human Brain Connectomes”, Et Al 2021
- “Decoupled Contrastive Learning”, Et Al 2021
- “Supervision Exists Everywhere: A Data Efficient Contrastive Language-Image Pre-training Paradigm (DeCLIP)”, Et Al 2021
-
“
THINGSvision: A Python Toolbox for Streamlining the Extraction of Activations From Deep Neural Networks”, 2021 - “Predicting Phenotypes from Genetic, Environment, Management, and Historical Data Using CNNs”, Et Al 2021
- “Dataset Distillation With Infinitely Wide Convolutional Networks”, Et Al 2021
- “Revisiting the Calibration of Modern Neural Networks”, Et Al 2021
- “Partial Success in Closing the Gap between Human and Machine Vision”, Et Al 2021
- “CoAtNet: Marrying Convolution and Attention for All Data Sizes”, Et Al 2021
- “Effect of Pre-Training Scale on Intra/Inter-Domain Full and Few-Shot Transfer Learning for Natural and Medical X-Ray Chest Images”, 2021
- “Predicting Sex from Retinal Fundus Photographs Using Automated Deep Learning”, Et Al 2021
- “Rethinking and Improving the Robustness of Image Style Transfer”, Et Al 2021
- “The Surprising Impact of Mask-head Architecture on Novel Class Segmentation”, Et Al 2021
- “Pervasive Label Errors in Test Sets Destabilize Machine Learning Benchmarks”, Et Al 2021
- “Learning from Videos to Understand the World”, Et Al 2021
- “Fast and Accurate Model Scaling”, Et Al 2021
- “Transfer of Fully Convolutional Policy-Value Networks Between Games and Game Variants”, Et Al 2021
- “Hiding Data Hiding”, Et Al 2021
- “NFNet: High-Performance Large-Scale Image Recognition Without Normalization”, Et Al 2021
- “Brain2Pix: Fully Convolutional Naturalistic Video Reconstruction from Brain Activity”, Et Al 2021
- “E(3)-Equivariant Graph Neural Networks for Data-Efficient and Accurate Interatomic Potentials”, Et Al 2021
- “Meta Pseudo Labels”, Et Al 2021
- “Converting Tabular Data into Images for Deep Learning With Convolutional Neural Networks”, Et Al 2021
- “Is MLP-Mixer a CNN in Disguise? As Part of This Blog Post, We Look at the MLP Mixer Architecture in Detail and Also Understand Why It Is Not Considered Convolution Free.”
- “Taming Transformers for High-Resolution Image Synthesis”, Et Al 2020
- “Ensemble Learning of Convolutional Neural Network, Support Vector Machine, and Best Linear Unbiased Predictor for Brain Age Prediction: ARAMIS Contribution to the Predictive Analytics Competition 2019 Challenge”, Couvy- Et Al 2020
- “Scaling down Deep Learning”, 2020
- “Very Deep VAEs Generalize Autoregressive Models and Can Outperform Them on Images”, 2020
- “Understanding RL Vision: With Diverse Environments, We Can Analyze, Diagnose and Edit Deep Reinforcement Learning Models Using Attribution”, Et Al 2020
- “Optimal Peanut Butter and Banana Sandwiches”, 2020
- “Accuracy and Performance Comparison of Video Action Recognition Approaches”, Et Al 2020
- “A Digital Biomarker of Diabetes from Smartphone-based Vascular Signals”, Et Al 2020
- “On Robustness and Transferability of Convolutional Neural Networks”, Et Al 2020
- “NVAE: A Deep Hierarchical Variational Autoencoder”, 2020
- “CoCoNuT: Combining Context-Aware Neural Translation Models Using Ensemble for Program Repair”, Et Al 2020
- “The Many Faces of Robustness: A Critical Analysis of Out-of-Distribution Generalization”, Et Al 2020
- “SimCLRv2: Big Self-Supervised Models Are Strong Semi-Supervised Learners”, Et Al 2020
- “FBNetV3: Joint Architecture-Recipe Search Using Predictor Pretraining”, Et Al 2020
- “Danny Hernandez on Forecasting and the Drivers of AI Progress”, Et Al 2020
- “Measuring the Algorithmic Efficiency of Neural Networks”, 2020
- “AI and Efficiency: We’re Releasing an Analysis Showing That Since 2012 the Amount of Compute Needed to Train a Neural Net to the Same Performance on ImageNet Classification Has Been Decreasing by a Factor of 2 Every 16 Months”, 2020
- “Reinforcement Learning With Augmented Data”, Et Al 2020
- “YOLOv4: Optimal Speed and Accuracy of Object Detection”, Et Al 2020
- “Scaling Laws from the Data Manifold Dimension”, 2020
- “Evolving Normalization-Activation Layers”, Et Al 2020
- “Conditional Convolutions for Instance Segmentation”, Et Al 2020
- “Rethinking Parameter Counting in Deep Models: Effective Dimensionality Revisited”, Et Al 2020
- “Bayesian Deep Learning and a Probabilistic Perspective of Generalization”, 2020
- “Do We Need Zero Training Loss After Achieving Zero Training Error?”, Et Al 2020
- “A Simple Framework for Contrastive Learning of Visual Representations”, Et Al 2020
- “Accelerating Feedforward Computation via Parallel Nonlinear Equation Solving”, Et Al 2020
- “First-in-human Evaluation of a Hand-held Automated Venipuncture Device for Rapid Venous Blood Draws”, Et Al 2020
- “Deep Double Descent: We Show That the Double Descent Phenomenon Occurs in CNNs, ResNets, and Transformers: Performance First Improves, Then Gets Worse, and Then Improves Again With Increasing Model Size, Data Size, or Training Time. This Effect Is Often Avoided through Careful Regularization. While This Behavior Appears to Be Fairly Universal, We Don’t yet Fully Understand Why It Happens, and View Further Study of This Phenomenon As an Important Research Direction.”, Et Al 2019
- “Anonymous Market Product Classification Based on Deep Learning”, Et Al 2019
- “The Origins and Prevalence of Texture Bias in Convolutional Neural Networks”, Et Al 2019
- “Taxonomy of Real Faults in Deep Learning Systems”, Et Al 2019
- “DD-PPO: Learning Near-Perfect PointGoal Navigators from 2.5 Billion Frames”, Et Al 2019
- “ObjectNet: A Large-scale Bias-controlled Dataset for Pushing the Limits of Object Recognition Models”, Et Al 2019
- “CAR: Learned Image Downscaling for Upscaling Using Content Adaptive Resampler”, 2019
- “Human-level Performance in 3D Multiplayer Games With Population-based Reinforcement Learning”, Et Al 2019
- “ImageNet-Sketch: Learning Robust Global Representations by Penalizing Local Predictive Power”, Et Al 2019
- “Improved Object Recognition Using Neural Networks Trained to Mimic the Brain’s Statistical Properties”, Et Al 2019
- “Neural System Identification With Neural Information Flow”, Et Al 2019
- “CutMix: Regularization Strategy to Train Strong Classifiers With Localizable Features”, Et Al 2019
- “Billion-scale Semi-supervised Learning for Image Classification”, Et Al 2019
- “NAS-FPN: Learning Scalable Feature Pyramid Architecture for Object Detection”, Et Al 2019
- “Benchmarking Neural Network Robustness to Common Corruptions and Perturbations”, 2019
- “Semantic Image Synthesis With Spatially-Adaptive Normalization”, Et Al 2019
- “The Bitter Lesson”, 2019
- “Learning To Follow Directions in Street View”, Et Al 2019
- “SuperTML: Two-Dimensional Word Embedding for the Precognition on Structured Tabular Data”, Et Al 2019
- “Real-time Continuous Transcription With Live Transcribe”, 2019
- “Do We Train on Test Data? Purging CIFAR of Near-Duplicates”, 2019
- “Pay Less Attention With Lightweight and Dynamic Convolutions”, Et Al 2019
- “Association Between Surgical Skin Markings in Dermoscopic Images and Diagnostic Performance of a Deep Learning Convolutional Neural Network for Melanoma Recognition”, Et Al 2019
- “Quantifying Generalization in Reinforcement Learning”, Et Al 2018
- “ProxylessNAS: Direct Neural Architecture Search on Target Task and Hardware”, Et Al 2018
- “ImageNet-trained CNNs Are Biased towards Texture; Increasing Shape Bias Improves Accuracy and Robustness”, Et Al 2018
- “Evolving Space-Time Neural Architectures for Videos”, Et Al 2018
- “StreetNet: Preference Learning With Convolutional Neural Network on Urban Crime Perception”, Et Al 2018
- “Understanding and Correcting Pathologies in the Training of Learned Optimizers”, Et Al 2018
- “Graph Convolutional Reinforcement Learning”, Et Al 2018
- “Don’t Give Me the Details, Just the Summary! Topic-Aware Convolutional Neural Networks for Extreme Summarization”, Et Al 2018
- “CurriculumNet: Weakly Supervised Learning from Large-Scale Web Images”, Et Al 2018
- “MnasNet: Platform-Aware Neural Architecture Search for Mobile”, Et Al 2018
- “Glow: Generative Flow With Invertible 1×1 Convolutions”, 2018
- “Benchmarking Neural Network Robustness to Common Corruptions and Surface Variations”, 2018
- “Confounding Variables Can Degrade Generalization Performance of Radiological Deep Learning Models”, Et Al 2018
- “Dynamical Isometry and a Mean Field Theory of CNNs: How to Train 10,000-Layer Vanilla Convolutional Neural Networks”, Et Al 2018
- “More Than a Feeling: Learning to Grasp and Regrasp Using Vision and Touch”, Et Al 2018
- “Deep Learning Generalizes Because the Parameter-function Map Is Biased towards Simple Functions”, Valle- Et Al 2018
- “BDD100K: A Diverse Driving Dataset for Heterogeneous Multitask Learning”, Et Al 2018
- “Tile2Vec: Unsupervised Representation Learning for Spatially Distributed Data”, Et Al 2018
- “Exploring the Limits of Weakly Supervised Pretraining”, Et Al 2018
- “YOLOv3: An Incremental Improvement”, 2018
- “Guess, Check and Fix: a Phenomenology of Improvisation In ‘Neural’ Painting”, 2018
- “Sim-to-Real Optimization of Complex Real World Mobile Network With Imperfect Information via Deep Reinforcement Learning from Self-play”, Et Al 2018
- “Evolved Policy Gradients”, Et Al 2018
- “Large-scale, High-resolution Comparison of the Core Visual Object Recognition Behavior of Humans, Monkeys, and State-of-the-art Deep Artificial Neural Networks”, Et Al 2018
- “IMPALA: Scalable Distributed Deep-RL With Importance Weighted Actor-Learner Architectures”, Et Al 2018
- “Active, Continual Fine Tuning of Convolutional Neural Networks for Reducing Annotation Efforts”, Et Al 2018
- “ArcFace: Additive Angular Margin Loss for Deep Face Recognition”, Et Al 2018
- “Man against Machine: Diagnostic Performance of a Deep Learning Convolutional Neural Network for Dermoscopic Melanoma Recognition in Comparison to 58 Dermatologists”, Et Al 2018
- “DeepGS: Predicting Phenotypes from Genotypes Using Deep Learning”, Et Al 2017
- “SPP-Net: Deep Absolute Pose Regression With Synthetic Views”, Et Al 2017
- “Measuring the Tendency of CNNs to Learn Surface Statistical Regularities”, 2017
- “BlockDrop: Dynamic Inference Paths in Residual Networks”, Et Al 2017
- “Knowledge Concentration: Learning 100K Object Classifiers in a Single CNN”, Et Al 2017
- “The Signature of Robot Action Success in EEG Signals of a Human Observer: Decoding and Visualization Using Deep Convolutional Neural Networks”, Et Al 2017
- “11K Hands: Gender Recognition and Biometric Identification Using a Large Dataset of Hand Images”, 2017
- “Learning to Play Chess With Minimal Lookahead and Deep Value Neural Networks”, 2017 (page 3)
- “High-Precision Automated Reconstruction of Neurons With Flood-filling Networks”, Et Al 2017
- “Efficient K-shot Learning With Regularized Deep Networks”, Et Al 2017
- “NIMA: Neural Image Assessment”, 2017
- “Squeeze-and-Excitation Networks”, Et Al 2017
- “What Does a Convolutional Neural Network Recognize in the Moon?”, 2017
- “SMASH: One-Shot Model Architecture Search through HyperNetworks”, Et Al 2017
- “A Deep Architecture for Unified Aesthetic Prediction”, 2017
- “Learning With Rethinking: Recurrently Improving Convolutional Neural Networks through Feedback”, Et Al 2017
- “WebVision Database: Visual Learning and Understanding from Web Data”, Et Al 2017
- “Focal Loss for Dense Object Detection”, Et Al 2017
- “Active Learning for Convolutional Neural Networks: A Core-Set Approach”, 2017
- “Learning to Infer Graphics Programs from Hand-Drawn Images”, Et Al 2017
- “Learning Transferable Architectures for Scalable Image Recognition”, Et Al 2017
- “Efficient Architecture Search by Network Transformation”, Et Al 2017
- “A Simple Neural Attentive Meta-Learner”, Et Al 2017
- “Towards Deep Learning Models Resistant to Adversarial Attacks”, Et Al 2017
- “A Simple Neural Network Module for Relational Reasoning”, Et Al 2017
- “What Makes a Good Image? Airbnb Demand Analytics Leveraging Interpretable Image Features”, Et Al 2017
- “Picasso: A Modular Framework for Visualizing the Learning Process of Neural Network Image Classifiers”, 2017
- “Adversarial Neural Machine Translation”, Et Al 2017
- “Multi-Scale Dense Networks for Resource Efficient Image Classification”, Et Al 2017
- “Mask R-CNN”, Et Al 2017
- “Using Human Brain Activity to Guide Machine Learning”, Et Al 2017
- “Learned Optimizers That Scale and Generalize”, Et Al 2017
- “Prediction and Control With Temporal Segment Models”, Et Al 2017
- “Parallel Multiscale Autoregressive Density Estimation”, Et Al 2017
- “Convolution Aware Initialization”, 2017
- “Gender-From-Iris or Gender-From-Mascara?”, Et Al 2017
- “BrainNetCNN: Convolutional Neural Networks for Brain Networks; towards Predicting Neurodevelopment”, Et Al 2017
- “PixelCNN++: Improving the PixelCNN With Discretized Logistic Mixture Likelihood and Other Modifications”, Et Al 2017
- “YOLO9000: Better, Faster, Stronger”, 2016
- “Language Modeling With Gated Convolutional Networks”, Et Al 2016
- “LipNet: End-to-End Sentence-level Lipreading”, Et Al 2016
- “Understanding Deep Learning Requires Rethinking Generalization”, Et Al 2016
- “Designing Neural Network Architectures Using Reinforcement Learning”, Et Al 2016
- “HyperNetworks”, Et Al 2016
- “Neural Photo Editing With Introspective Adversarial Networks”, Et Al 2016
- “Direct Feedback Alignment Provides Learning in Deep Neural Networks”, 2016
- “Deep Learning Human Mind for Automated Visual Classification”, Et Al 2016
- “DenseNet: Densely Connected Convolutional Networks”, Et Al 2016
- “Deep Learning the City: Quantifying Urban Perception At A Global Scale”, Et Al 2016
- “Convolutional Neural Fabrics”, 2016
- “FractalNet: Ultra-Deep Neural Networks without Residuals”, Et Al 2016
- “ViZDoom: A Doom-based AI Research Platform for Visual Reinforcement Learning”, Et Al 2016
- “OHEM: Training Region-based Object Detectors With Online Hard Example Mining”, Et Al 2016
- “Deep Networks With Stochastic Depth”, Et Al 2016
- “Convolutional Networks for Fast, Energy-Efficient Neuromorphic Computing”, Et Al 2016
- “Do Deep Convolutional Nets Really Need to Be Deep and Convolutional?”, Et Al 2016
- “Learning Hand-Eye Coordination for Robotic Grasping With Deep Learning and Large-Scale Data Collection”, Et Al 2016
- “Network Morphism”, Et Al 2016
- “Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning”, Et Al 2016
- “PlaNet—Photo Geolocation With Convolutional Neural Networks”, Et Al 2016
- “Value Iteration Networks”, Et Al 2016
- “Pixel Recurrent Neural Networks”, Et Al 2016
-
“Image Synthesis from Yahoo’s
open_nsfw”, 2016 - “Deep Residual Learning for Image Recognition”, Et Al 2015
- “Adding Gradient Noise Improves Learning for Very Deep Networks”, Et Al 2015
- “Learning Visual Features from Large Weakly Supervised Data”, Et Al 2015
- “Predicting and Understanding Urban Perception With Convolutional Neural Networks”, Et Al 2015
- “LSUN: Construction of a Large-scale Image Dataset Using Deep Learning With Humans in the Loop”, Et Al 2015
- “You Only Look Once: Unified, Real-Time Object Detection”, Et Al 2015
- “Clothing-1M: Learning from Massive Noisy Labeled Data for Image Classification”, Et Al 2015
- “Faster R-CNN: Towards Real-Time Object Detection With Region Proposal Networks”, Et Al 2015
- “Cyclical Learning Rates for Training Neural Networks”, 2015
- “Deep Learning”, Et Al 2015
- “Fast R-CNN”, 2015
- “End-to-End Training of Deep Visuomotor Policies”, Et Al 2015
- “FaceNet: A Unified Embedding for Face Recognition and Clustering”, Et Al 2015
- “DeepID3: Face Recognition With Very Deep Neural Networks”, Et Al 2015
- “Understanding Image Representations by Measuring Their Equivariance and Equivalence”, 2014
- “Going Deeper With Convolutions”, Et Al 2014
- “Very Deep Convolutional Networks for Large-Scale Image Recognition”, 2014
- “ImageNet Large Scale Visual Recognition Challenge”, Et Al 2014
- “Deep Learning Face Representation by Joint Identification-Verification”, Et Al 2014
- “ImageNet Classification With Deep Convolutional Neural Networks”, Et Al 2012
- “Multi-column Deep Neural Network for Traffic Sign Classification”, Cireşan Et Al 2012
- “Multi-column Deep Neural Networks for Image Classification”, Cireşan Et Al 2012
- “DanNet: Flexible, High Performance Convolutional Neural Networks for Image Classification”, Et Al 2011
- Wikipedia
- Miscellaneous
- Link Bibliography
See Also
Links
“Does Progress on ImageNet Transfer to Real-world Datasets?”, Et Al 2023
“Does progress on ImageNet transfer to real-world datasets?”, 2023-01-11 (similar)
“Pruning Compact ConvNets for Efficient Inference”, Et Al 2023
“Pruning Compact ConvNets for Efficient Inference”, 2023-01-11 (; similar)
“EarSpy: Spying Caller Speech and Identity through Tiny Vibrations of Smartphone Ear Speakers”, Et Al 2022
“EarSpy: Spying Caller Speech and Identity through Tiny Vibrations of Smartphone Ear Speakers”, 2022-12-23 (; similar)
“What Do Vision Transformers Learn? A Visual Exploration”, Et Al 2022
“What do Vision Transformers Learn? A Visual Exploration”, 2022-12-13 (; similar; bibliography)
“Simulated Automated Facial Recognition Systems As Decision-aids in Forensic Face Matching Tasks”, 2022
“Simulated automated facial recognition systems as decision-aids in forensic face matching tasks”, 2022-12 (; similar; bibliography)
“Predicting Sex, Age, General Cognition and Mental Health With Machine Learning on Brain Structural Connectomes”, Et Al 2022
“Predicting sex, age, general cognition and mental health with machine learning on brain structural connectomes”, 2022-11-22 (; similar)
“Interpreting Neural Networks through the Polytope Lens”, Et Al 2022
“Interpreting Neural Networks through the Polytope Lens”, 2022-11-22 (; similar)
“The Power of Ensembles for Active Learning in Image Classification”, Et Al 2022
“The Power of Ensembles for Active Learning in Image Classification”, 2022-11-10 (; similar)
“GCN-Based Multi-Modal Multi-Label Attribute Classification in Anime Illustration Using Domain-Specific Semantic Features”, Et Al 2022
“GCN-Based Multi-Modal Multi-Label Attribute Classification in Anime Illustration Using Domain-Specific Semantic Features”, 2022-10-16 (; bibliography)
“The Unreasonable Effectiveness of Fully-Connected Layers for Low-Data Regimes”, Et Al 2022
“The Unreasonable Effectiveness of Fully-Connected Layers for Low-Data Regimes”, 2022-10-11 (; similar)
“Understanding the Covariance Structure of Convolutional Filters”, Et Al 2022
“Understanding the Covariance Structure of Convolutional Filters”, 2022-10-07 (backlinks; similar; bibliography)
“VICRegL: Self-Supervised Learning of Local Visual Features”, Et Al 2022
“VICRegL: Self-Supervised Learning of Local Visual Features”, 2022-10-04 (similar)
“FastSiam: Resource-Efficient Self-supervised Learning on a Single GPU”, Et Al 2022
“FastSiam: Resource-Efficient Self-supervised Learning on a Single GPU”, 2022-09-20 (similar; bibliography)
“Evaluation of Transfer Learning Methods for Detecting Alzheimer’s Disease With Brain MRI”, Et Al 2022
“Evaluation of Transfer Learning Methods for Detecting Alzheimer’s Disease with Brain MRI”, 2022-08-25 (; similar)
“Reassessing Hierarchical Correspondences between Brain and Deep Networks through Direct Interface”, 2022
“Reassessing hierarchical correspondences between brain and deep networks through direct interface”, 2022-07-13 (; similar; bibliography)
“Timesweeper: Accurately Identifying Selective Sweeps Using Population Genomic Time Series”, 2022
“Timesweeper: Accurately Identifying Selective Sweeps Using Population Genomic Time Series”, 2022-07-07 (; similar)
“RHO-LOSS: Prioritized Training on Points That Are Learnable, Worth Learning, and Not Yet Learnt”, Et Al 2022
“RHO-LOSS: Prioritized Training on Points that are Learnable, Worth Learning, and Not Yet Learnt”, 2022-06-14 (; similar; bibliography)
“BigVGAN: A Universal Neural Vocoder With Large-Scale Training”, Et Al 2022
“BigVGAN: A Universal Neural Vocoder with Large-Scale Training”, 2022-06-09 (; similar; bibliography)
“Continual Pre-Training Mitigates Forgetting in Language and Vision”, Et Al 2022
“Continual Pre-Training Mitigates Forgetting in Language and Vision”, 2022-05-19 (; backlinks; similar)
“Scaling Up Your Kernels to 31×31: Revisiting Large Kernel Design in CNNs (RepLKNet)”, Et Al 2022
“Scaling Up Your Kernels to 31×31: Revisiting Large Kernel Design in CNNs (RepLKNet)”, 2022-03-13 (backlinks; similar; bibliography)
“Variational Autoencoders Without the Variation”, Et Al 2022
“Variational Autoencoders Without the Variation”, 2022-03-01 (; similar)
“On the Effectiveness of Dataset Watermarking in Adversarial Settings”, 2022
“On the Effectiveness of Dataset Watermarking in Adversarial Settings”, 2022-02-25 (; similar)
“Approximating CNNs With Bag-of-local-Features Models Works Surprisingly Well on ImageNet”, Et Al 2022
“Approximating CNNs with Bag-of-local-Features models works surprisingly well on ImageNet”, 2022-02-10 (backlinks; similar; bibliography)
“ConvMixer: Patches Are All You Need?”, 2022
“ConvMixer: Patches Are All You Need?”, 2022-01-24 (; backlinks; similar; bibliography)
“HyperTransformer: Model Generation for Supervised and Semi-Supervised Few-Shot Learning”, Et Al 2022
“HyperTransformer: Model Generation for Supervised and Semi-Supervised Few-Shot Learning”, 2022-01-11 (; similar)
“ConvNeXt: A ConvNet for the 2020s”, Et Al 2022
“ConvNeXt: A ConvNet for the 2020s”, 2022-01-10 (similar; bibliography)
“An Empirical Investigation of the Role of Pre-training in Lifelong Learning”, Et Al 2021
“An Empirical Investigation of the Role of Pre-training in Lifelong Learning”, 2021-12-16 (; backlinks; similar)
“Noether Networks: Meta-Learning Useful Conserved Quantities”, Et Al 2021
“Noether Networks: Meta-Learning Useful Conserved Quantities”, 2021-12-06 (; similar)
“The Efficiency Misnomer”, Et Al 2021
“The Efficiency Misnomer”, 2021-10-25 (; similar)
“Evaluating Loss Functions for Illustration Super-Resolution Neural Networks”, 2021
“Evaluating Loss Functions for Illustration Super-Resolution Neural Networks”, 2021-10-18 (; similar)
“Deep Learning Models of Cognitive Processes Constrained by Human Brain Connectomes”, Et Al 2021
“Deep learning models of cognitive processes constrained by human brain connectomes”, 2021-10-14 (; similar)
“Decoupled Contrastive Learning”, Et Al 2021
“Decoupled Contrastive Learning”, 2021-10-13 (similar; bibliography)
“Supervision Exists Everywhere: A Data Efficient Contrastive Language-Image Pre-training Paradigm (DeCLIP)”, Et Al 2021
“Supervision Exists Everywhere: A Data Efficient Contrastive Language-Image Pre-training Paradigm (DeCLIP)”, 2021-10-11 (; similar; bibliography)
“THINGSvision: A Python Toolbox for Streamlining the Extraction of Activations From Deep Neural Networks”, 2021
“THINGSvision: A Python Toolbox for Streamlining the Extraction of Activations From Deep Neural Networks”, 2021-09-22 (; similar; bibliography)
“Predicting Phenotypes from Genetic, Environment, Management, and Historical Data Using CNNs”, Et Al 2021
“Predicting phenotypes from genetic, environment, management, and historical data using CNNs”, 2021-08-27 (; similar)
“Dataset Distillation With Infinitely Wide Convolutional Networks”, Et Al 2021
“Dataset Distillation with Infinitely Wide Convolutional Networks”, 2021-07-27 (; similar)
“Revisiting the Calibration of Modern Neural Networks”, Et Al 2021
“Revisiting the Calibration of Modern Neural Networks”, 2021-06-15 (; similar)
“Partial Success in Closing the Gap between Human and Machine Vision”, Et Al 2021
“Partial success in closing the gap between human and machine vision”, 2021-06-14 (; backlinks; similar; bibliography)
“CoAtNet: Marrying Convolution and Attention for All Data Sizes”, Et Al 2021
“CoAtNet: Marrying Convolution and Attention for All Data Sizes”, 2021-06-09 (; similar; bibliography)
“Effect of Pre-Training Scale on Intra/Inter-Domain Full and Few-Shot Transfer Learning for Natural and Medical X-Ray Chest Images”, 2021
“Effect of Pre-Training Scale on Intra/Inter-Domain Full and Few-Shot Transfer Learning for Natural and Medical X-Ray Chest Images”, 2021-05-31 (; similar; bibliography)
“Predicting Sex from Retinal Fundus Photographs Using Automated Deep Learning”, Et Al 2021
“Predicting sex from retinal fundus photographs using automated deep learning”, 2021-05-13 (; backlinks; similar)
“Rethinking and Improving the Robustness of Image Style Transfer”, Et Al 2021
“Rethinking and Improving the Robustness of Image Style Transfer”, 2021-04-08 (similar; bibliography)
“The Surprising Impact of Mask-head Architecture on Novel Class Segmentation”, Et Al 2021
“The surprising impact of mask-head architecture on novel class segmentation”, 2021-04-01 (similar; bibliography)
“Pervasive Label Errors in Test Sets Destabilize Machine Learning Benchmarks”, Et Al 2021
“Pervasive Label Errors in Test Sets Destabilize Machine Learning Benchmarks”, 2021-03-26 (; similar; bibliography)
“Learning from Videos to Understand the World”, Et Al 2021
“Learning from videos to understand the world”, 2021-03-12 (; similar; bibliography)
“Fast and Accurate Model Scaling”, Et Al 2021
“Fast and Accurate Model Scaling”, 2021-03-11 (; similar)
“Transfer of Fully Convolutional Policy-Value Networks Between Games and Game Variants”, Et Al 2021
“Transfer of Fully Convolutional Policy-Value Networks Between Games and Game Variants”, 2021-02-24 (; similar)
“Hiding Data Hiding”, Et Al 2021
“Hiding Data Hiding”, 2021-02-13 (; similar)
“NFNet: High-Performance Large-Scale Image Recognition Without Normalization”, Et Al 2021
“NFNet: High-Performance Large-Scale Image Recognition Without Normalization”, 2021-02-11 (; similar; bibliography)
“Brain2Pix: Fully Convolutional Naturalistic Video Reconstruction from Brain Activity”, Et Al 2021
“Brain2Pix: Fully convolutional naturalistic video reconstruction from brain activity”, 2021-02-03 (; similar)
“E(3)-Equivariant Graph Neural Networks for Data-Efficient and Accurate Interatomic Potentials”, Et Al 2021
“E(3)-Equivariant Graph Neural Networks for Data-Efficient and Accurate Interatomic Potentials”, 2021-01-08 (; backlinks; similar)
“Meta Pseudo Labels”, Et Al 2021
“Meta Pseudo Labels”, 2021-01-05 (; similar; bibliography)
“Converting Tabular Data into Images for Deep Learning With Convolutional Neural Networks”, Et Al 2021
“Converting tabular data into images for deep learning with convolutional neural networks”, 2021 (; similar)
“Is MLP-Mixer a CNN in Disguise? As Part of This Blog Post, We Look at the MLP Mixer Architecture in Detail and Also Understand Why It Is Not Considered Convolution Free.”
“Taming Transformers for High-Resolution Image Synthesis”, Et Al 2020
“Taming Transformers for High-Resolution Image Synthesis”, 2020-12-17 (; backlinks; similar)
“Ensemble Learning of Convolutional Neural Network, Support Vector Machine, and Best Linear Unbiased Predictor for Brain Age Prediction: ARAMIS Contribution to the Predictive Analytics Competition 2019 Challenge”, Couvy- Et Al 2020
“Ensemble Learning of Convolutional Neural Network, Support Vector Machine, and Best Linear Unbiased Predictor for Brain Age Prediction: ARAMIS Contribution to the Predictive Analytics Competition 2019 Challenge”, 2020-12-15 (; backlinks; similar)
“Scaling down Deep Learning”, 2020
“Scaling down Deep Learning”, 2020-12-01 (; backlinks; similar; bibliography)
“Very Deep VAEs Generalize Autoregressive Models and Can Outperform Them on Images”, 2020
“Very Deep VAEs Generalize Autoregressive Models and Can Outperform Them on Images”, 2020-11-20 (; similar; bibliography)
“Understanding RL Vision: With Diverse Environments, We Can Analyze, Diagnose and Edit Deep Reinforcement Learning Models Using Attribution”, Et Al 2020
“Understanding RL Vision: With diverse environments, we can analyze, diagnose and edit deep reinforcement learning models using attribution”, 2020-11-17 (; similar)
“Optimal Peanut Butter and Banana Sandwiches”, 2020
“Optimal Peanut Butter and Banana Sandwiches”, 2020-08-25 (; similar; bibliography)
“Accuracy and Performance Comparison of Video Action Recognition Approaches”, Et Al 2020
“Accuracy and Performance Comparison of Video Action Recognition Approaches”, 2020-08-20 (; similar; bibliography)
“A Digital Biomarker of Diabetes from Smartphone-based Vascular Signals”, Et Al 2020
“A digital biomarker of diabetes from smartphone-based vascular signals”, 2020-08-17 (; similar)
“On Robustness and Transferability of Convolutional Neural Networks”, Et Al 2020
“On Robustness and Transferability of Convolutional Neural Networks”, 2020-07-16 (; similar)
“NVAE: A Deep Hierarchical Variational Autoencoder”, 2020
“NVAE: A Deep Hierarchical Variational Autoencoder”, 2020-07-08 (; similar; bibliography)
“CoCoNuT: Combining Context-Aware Neural Translation Models Using Ensemble for Program Repair”, Et Al 2020
“CoCoNuT: Combining Context-Aware Neural Translation Models using Ensemble for Program Repair”, 2020-07-01 (; backlinks; similar)
“The Many Faces of Robustness: A Critical Analysis of Out-of-Distribution Generalization”, Et Al 2020
“The Many Faces of Robustness: A Critical Analysis of Out-of-Distribution Generalization”, 2020-06-29 (; backlinks; similar)
“SimCLRv2: Big Self-Supervised Models Are Strong Semi-Supervised Learners”, Et Al 2020
“SimCLRv2: Big Self-Supervised Models are Strong Semi-Supervised Learners”, 2020-06-17 (; similar)
“FBNetV3: Joint Architecture-Recipe Search Using Predictor Pretraining”, Et Al 2020
“FBNetV3: Joint Architecture-Recipe Search using Predictor Pretraining”, 2020-06-03 (; similar)
“Danny Hernandez on Forecasting and the Drivers of AI Progress”, Et Al 2020
“Danny Hernandez on forecasting and the drivers of AI progress”, 2020-05-22 (; similar)
“Measuring the Algorithmic Efficiency of Neural Networks”, 2020
“Measuring the Algorithmic Efficiency of Neural Networks”, 2020-05-08 (; similar)
“AI and Efficiency: We’re Releasing an Analysis Showing That Since 2012 the Amount of Compute Needed to Train a Neural Net to the Same Performance on ImageNet Classification Has Been Decreasing by a Factor of 2 Every 16 Months”, 2020
“AI and Efficiency: We’re releasing an analysis showing that since 2012 the amount of compute needed to train a neural net to the same performance on ImageNet classification has been decreasing by a factor of 2 every 16 months”, 2020-05-05 (; backlinks; similar)
“Reinforcement Learning With Augmented Data”, Et Al 2020
“Reinforcement Learning with Augmented Data”, 2020-04-30 (; similar)
“YOLOv4: Optimal Speed and Accuracy of Object Detection”, Et Al 2020
“YOLOv4: Optimal Speed and Accuracy of Object Detection”, 2020-04-23 (backlinks; similar; bibliography)
“Evolving Normalization-Activation Layers”, Et Al 2020
“Evolving Normalization-Activation Layers”, 2020-04-06 (; similar)
“Conditional Convolutions for Instance Segmentation”, Et Al 2020
“Conditional Convolutions for Instance Segmentation”, 2020-03-12 (backlinks; similar)
“Rethinking Parameter Counting in Deep Models: Effective Dimensionality Revisited”, Et Al 2020
“Rethinking Parameter Counting in Deep Models: Effective Dimensionality Revisited”, 2020-03-04 (; backlinks; similar)
“Bayesian Deep Learning and a Probabilistic Perspective of Generalization”, 2020
“Bayesian Deep Learning and a Probabilistic Perspective of Generalization”, 2020-02-20 (; backlinks; similar)
“Do We Need Zero Training Loss After Achieving Zero Training Error?”, Et Al 2020
“Do We Need Zero Training Loss After Achieving Zero Training Error?”, 2020-02-20 (; backlinks; similar)
“A Simple Framework for Contrastive Learning of Visual Representations”, Et Al 2020
“A Simple Framework for Contrastive Learning of Visual Representations”, 2020-02-13 (; similar; bibliography)
“Accelerating Feedforward Computation via Parallel Nonlinear Equation Solving”, Et Al 2020
“Accelerating Feedforward Computation via Parallel Nonlinear Equation Solving”, 2020-02-10 (; similar; bibliography)
“First-in-human Evaluation of a Hand-held Automated Venipuncture Device for Rapid Venous Blood Draws”, Et Al 2020
“First-in-human evaluation of a hand-held automated venipuncture device for rapid venous blood draws”, 2020-01-22 (; similar)
“Deep Double Descent: We Show That the Double Descent Phenomenon Occurs in CNNs, ResNets, and Transformers: Performance First Improves, Then Gets Worse, and Then Improves Again With Increasing Model Size, Data Size, or Training Time. This Effect Is Often Avoided through Careful Regularization. While This Behavior Appears to Be Fairly Universal, We Don’t yet Fully Understand Why It Happens, and View Further Study of This Phenomenon As an Important Research Direction.”, Et Al 2019
“Deep Double Descent: We show that the double descent phenomenon occurs in CNNs, ResNets, and transformers: performance first improves, then gets worse, and then improves again with increasing model size, data size, or training time. This effect is often avoided through careful regularization. While this behavior appears to be fairly universal, we don’t yet fully understand why it happens, and view further study of this phenomenon as an important research direction.”, 2019-12-05 (; backlinks; similar; bibliography)
“Anonymous Market Product Classification Based on Deep Learning”, Et Al 2019
“Anonymous market product classification based on deep learning”, 2019-12 (; backlinks; similar)
“The Origins and Prevalence of Texture Bias in Convolutional Neural Networks”, Et Al 2019
“The Origins and Prevalence of Texture Bias in Convolutional Neural Networks”, 2019-11-20 (backlinks; similar)
“Taxonomy of Real Faults in Deep Learning Systems”, Et Al 2019
“Taxonomy of Real Faults in Deep Learning Systems”, 2019-11-07 (; backlinks; similar)
“DD-PPO: Learning Near-Perfect PointGoal Navigators from 2.5 Billion Frames”, Et Al 2019
“DD-PPO: Learning Near-Perfect PointGoal Navigators from 2.5 Billion Frames”, 2019-11-01 (; similar; bibliography)
“ObjectNet: A Large-scale Bias-controlled Dataset for Pushing the Limits of Object Recognition Models”, Et Al 2019
“ObjectNet: A large-scale bias-controlled dataset for pushing the limits of object recognition models”, 2019-09-06 (; backlinks; similar)
“CAR: Learned Image Downscaling for Upscaling Using Content Adaptive Resampler”, 2019
“CAR: Learned Image Downscaling for Upscaling using Content Adaptive Resampler”, 2019-07-22 (backlinks; similar)
“Human-level Performance in 3D Multiplayer Games With Population-based Reinforcement Learning”, Et Al 2019
“Human-level performance in 3D multiplayer games with population-based reinforcement learning”, 2019-05-31 (; similar; bibliography)
“ImageNet-Sketch: Learning Robust Global Representations by Penalizing Local Predictive Power”, Et Al 2019
“ImageNet-Sketch: Learning Robust Global Representations by Penalizing Local Predictive Power”, 2019-05-29 (; backlinks; similar)
“Improved Object Recognition Using Neural Networks Trained to Mimic the Brain’s Statistical Properties”, Et Al 2019
“Improved object recognition using neural networks trained to mimic the brain’s statistical properties”, 2019-05-25 (; similar)
“Neural System Identification With Neural Information Flow”, Et Al 2019
“Neural System Identification with Neural Information Flow”, 2019-05-23 (; similar)
“CutMix: Regularization Strategy to Train Strong Classifiers With Localizable Features”, Et Al 2019
“CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features”, 2019-05-13 (backlinks; similar; bibliography)
“Billion-scale Semi-supervised Learning for Image Classification”, Et Al 2019
“Billion-scale semi-supervised learning for image classification”, 2019-05-02 (; similar; bibliography)
“NAS-FPN: Learning Scalable Feature Pyramid Architecture for Object Detection”, Et Al 2019
“NAS-FPN: Learning Scalable Feature Pyramid Architecture for Object Detection”, 2019-04-16 (backlinks; similar)
“Benchmarking Neural Network Robustness to Common Corruptions and Perturbations”, 2019
“Benchmarking Neural Network Robustness to Common Corruptions and Perturbations”, 2019-03-28 (; backlinks; similar)
“Semantic Image Synthesis With Spatially-Adaptive Normalization”, Et Al 2019
“Semantic Image Synthesis with Spatially-Adaptive Normalization”, 2019-03-18 (backlinks; similar)
“The Bitter Lesson”, 2019
“The Bitter Lesson”, 2019-03-13 (; backlinks; similar)
“Learning To Follow Directions in Street View”, Et Al 2019
“Learning To Follow Directions in Street View”, 2019-03-01 (; similar)
“SuperTML: Two-Dimensional Word Embedding for the Precognition on Structured Tabular Data”, Et Al 2019
“SuperTML: Two-Dimensional Word Embedding for the Precognition on Structured Tabular Data”, 2019-02-26 (; similar)
“Real-time Continuous Transcription With Live Transcribe”, 2019
“Real-time Continuous Transcription with Live Transcribe”, 2019-02-04 (; similar)
“Do We Train on Test Data? Purging CIFAR of Near-Duplicates”, 2019
“Do We Train on Test Data? Purging CIFAR of Near-Duplicates”, 2019-02-01 (; similar)
“Pay Less Attention With Lightweight and Dynamic Convolutions”, Et Al 2019
“Pay Less Attention with Lightweight and Dynamic Convolutions”, 2019-01-29 (; similar)
“Association Between Surgical Skin Markings in Dermoscopic Images and Diagnostic Performance of a Deep Learning Convolutional Neural Network for Melanoma Recognition”, Et Al 2019
“Quantifying Generalization in Reinforcement Learning”, Et Al 2018
“Quantifying Generalization in Reinforcement Learning”, 2018-12-06 (; similar)
“ProxylessNAS: Direct Neural Architecture Search on Target Task and Hardware”, Et Al 2018
“ProxylessNAS: Direct Neural Architecture Search on Target Task and Hardware”, 2018-12-02 (; backlinks; similar)
“ImageNet-trained CNNs Are Biased towards Texture; Increasing Shape Bias Improves Accuracy and Robustness”, Et Al 2018
“ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness”, 2018-11-29 (; backlinks; similar)
“Evolving Space-Time Neural Architectures for Videos”, Et Al 2018
“Evolving Space-Time Neural Architectures for Videos”, 2018-11-26 (; backlinks; similar)
“StreetNet: Preference Learning With Convolutional Neural Network on Urban Crime Perception”, Et Al 2018
“StreetNet: Preference Learning with Convolutional Neural Network on Urban Crime Perception”, 2018-11-01 (; backlinks; similar; bibliography)
“Understanding and Correcting Pathologies in the Training of Learned Optimizers”, Et Al 2018
“Understanding and correcting pathologies in the training of learned optimizers”, 2018-10-24 (; backlinks; similar)
“Graph Convolutional Reinforcement Learning”, Et Al 2018
“Graph Convolutional Reinforcement Learning”, 2018-10-22 (; similar)
“Don’t Give Me the Details, Just the Summary! Topic-Aware Convolutional Neural Networks for Extreme Summarization”, Et Al 2018
“Don’t Give Me the Details, Just the Summary! Topic-Aware Convolutional Neural Networks for Extreme Summarization”, 2018-08-27 (; backlinks; similar)
“CurriculumNet: Weakly Supervised Learning from Large-Scale Web Images”, Et Al 2018
“CurriculumNet: Weakly Supervised Learning from Large-Scale Web Images”, 2018-08-03 (; backlinks; similar; bibliography)
“MnasNet: Platform-Aware Neural Architecture Search for Mobile”, Et Al 2018
“MnasNet: Platform-Aware Neural Architecture Search for Mobile”, 2018-07-31 (backlinks; similar)
“Glow: Generative Flow With Invertible 1×1 Convolutions”, 2018
“Glow: Generative Flow with Invertible 1×1 Convolutions”, 2018-07-09 (backlinks; similar)
“Benchmarking Neural Network Robustness to Common Corruptions and Surface Variations”, 2018
“Benchmarking Neural Network Robustness to Common Corruptions and Surface Variations”, 2018-07-04 (; backlinks; similar)
“Confounding Variables Can Degrade Generalization Performance of Radiological Deep Learning Models”, Et Al 2018
“Confounding variables can degrade generalization performance of radiological deep learning models”, 2018-07-02 (backlinks; similar)
“Dynamical Isometry and a Mean Field Theory of CNNs: How to Train 10,000-Layer Vanilla Convolutional Neural Networks”, Et Al 2018
“Dynamical Isometry and a Mean Field Theory of CNNs: How to Train 10,000-Layer Vanilla Convolutional Neural Networks”, 2018-06-14 (backlinks; similar)
“More Than a Feeling: Learning to Grasp and Regrasp Using Vision and Touch”, Et Al 2018
“More Than a Feeling: Learning to Grasp and Regrasp using Vision and Touch”, 2018-05-28 (; similar)
“Deep Learning Generalizes Because the Parameter-function Map Is Biased towards Simple Functions”, Valle- Et Al 2018
“Deep learning generalizes because the parameter-function map is biased towards simple functions”, 2018-05-22 (; similar)
“BDD100K: A Diverse Driving Dataset for Heterogeneous Multitask Learning”, Et Al 2018
“BDD100K: A Diverse Driving Dataset for Heterogeneous Multitask Learning”, 2018-05-12 (; backlinks; similar)
“Tile2Vec: Unsupervised Representation Learning for Spatially Distributed Data”, Et Al 2018
“Tile2Vec: Unsupervised representation learning for spatially distributed data”, 2018-05-08 (backlinks; similar)
“Exploring the Limits of Weakly Supervised Pretraining”, Et Al 2018
“Exploring the Limits of Weakly Supervised Pretraining”, 2018-05-02 (; similar; bibliography)
“YOLOv3: An Incremental Improvement”, 2018
“YOLOv3: An Incremental Improvement”, 2018-04-08 (backlinks; similar; bibliography)
“Guess, Check and Fix: a Phenomenology of Improvisation In ‘Neural’ Painting”, 2018
“Guess, check and fix: a phenomenology of improvisation in ‘neural’ painting”, 2018-02-22 (similar; bibliography)
“Sim-to-Real Optimization of Complex Real World Mobile Network With Imperfect Information via Deep Reinforcement Learning from Self-play”, Et Al 2018
“Sim-to-Real Optimization of Complex Real World Mobile Network with Imperfect Information via Deep Reinforcement Learning from Self-play”, 2018-02-18 (; similar)
“Evolved Policy Gradients”, Et Al 2018
“Evolved Policy Gradients”, 2018-02-13 (; similar)
“Large-scale, High-resolution Comparison of the Core Visual Object Recognition Behavior of Humans, Monkeys, and State-of-the-art Deep Artificial Neural Networks”, Et Al 2018
“Large-scale, high-resolution comparison of the core visual object recognition behavior of humans, monkeys, and state-of-the-art deep artificial neural networks”, 2018-02-12 (; similar)
“IMPALA: Scalable Distributed Deep-RL With Importance Weighted Actor-Learner Architectures”, Et Al 2018
“IMPALA: Scalable Distributed Deep-RL with Importance Weighted Actor-Learner Architectures”, 2018-02-05 (; similar)
“Active, Continual Fine Tuning of Convolutional Neural Networks for Reducing Annotation Efforts”, Et Al 2018
“Active, Continual Fine Tuning of Convolutional Neural Networks for Reducing Annotation Efforts”, 2018-02-03 (; similar)
“ArcFace: Additive Angular Margin Loss for Deep Face Recognition”, Et Al 2018
“ArcFace: Additive Angular Margin Loss for Deep Face Recognition”, 2018-01-23 (backlinks; similar)
“Man against Machine: Diagnostic Performance of a Deep Learning Convolutional Neural Network for Dermoscopic Melanoma Recognition in Comparison to 58 Dermatologists”, Et Al 2018
“DeepGS: Predicting Phenotypes from Genotypes Using Deep Learning”, Et Al 2017
“DeepGS: Predicting phenotypes from genotypes using Deep Learning”, 2017-12-31 (; similar)
“SPP-Net: Deep Absolute Pose Regression With Synthetic Views”, Et Al 2017
“SPP-Net: Deep Absolute Pose Regression with Synthetic Views”, 2017-12-09 (backlinks; similar)
“Measuring the Tendency of CNNs to Learn Surface Statistical Regularities”, 2017
“Measuring the tendency of CNNs to Learn Surface Statistical Regularities”, 2017-11-30 (backlinks; similar)
“BlockDrop: Dynamic Inference Paths in Residual Networks”, Et Al 2017
“BlockDrop: Dynamic Inference Paths in Residual Networks”, 2017-11-22 (; backlinks; similar)
“Knowledge Concentration: Learning 100K Object Classifiers in a Single CNN”, Et Al 2017
“Knowledge Concentration: Learning 100K Object Classifiers in a Single CNN”, 2017-11-21 (; similar)
“The Signature of Robot Action Success in EEG Signals of a Human Observer: Decoding and Visualization Using Deep Convolutional Neural Networks”, Et Al 2017
“The signature of robot action success in EEG signals of a human observer: Decoding and visualization using deep convolutional neural networks”, 2017-11-16 (; similar)
“11K Hands: Gender Recognition and Biometric Identification Using a Large Dataset of Hand Images”, 2017
“11K Hands: Gender recognition and biometric identification using a large dataset of hand images”, 2017-11-12 (; backlinks; similar)
“Learning to Play Chess With Minimal Lookahead and Deep Value Neural Networks”, 2017 (page 3)
“Learning to Play Chess with Minimal Lookahead and Deep Value Neural Networks”, 2017-10-30 (; similar; bibliography)
“High-Precision Automated Reconstruction of Neurons With Flood-filling Networks”, Et Al 2017
“High-Precision Automated Reconstruction of Neurons with Flood-filling Networks”, 2017-10-09 (; backlinks; similar)
“Efficient K-shot Learning With Regularized Deep Networks”, Et Al 2017
“Efficient K-shot Learning with Regularized Deep Networks”, 2017-10-06 (; backlinks; similar)
“NIMA: Neural Image Assessment”, 2017
“NIMA: Neural Image Assessment”, 2017-09-15 (; similar)
“Squeeze-and-Excitation Networks”, Et Al 2017
“Squeeze-and-Excitation Networks”, 2017-09-05 (backlinks; similar)
“What Does a Convolutional Neural Network Recognize in the Moon?”, 2017
“What does a convolutional neural network recognize in the moon?”, 2017-08-18 (similar)
“SMASH: One-Shot Model Architecture Search through HyperNetworks”, Et Al 2017
“SMASH: One-Shot Model Architecture Search through HyperNetworks”, 2017-08-17 (; backlinks; similar; bibliography)
“A Deep Architecture for Unified Aesthetic Prediction”, 2017
“A deep architecture for unified aesthetic prediction”, 2017-08-16 (; backlinks; similar)
“Learning With Rethinking: Recurrently Improving Convolutional Neural Networks through Feedback”, Et Al 2017
“Learning with Rethinking: Recurrently Improving Convolutional Neural Networks through Feedback”, 2017-08-15 (backlinks; similar)
“WebVision Database: Visual Learning and Understanding from Web Data”, Et Al 2017
“WebVision Database: Visual Learning and Understanding from Web Data”, 2017-08-09 (; backlinks; similar)
“Focal Loss for Dense Object Detection”, Et Al 2017
“Focal Loss for Dense Object Detection”, 2017-08-07 (similar)
“Active Learning for Convolutional Neural Networks: A Core-Set Approach”, 2017
“Active Learning for Convolutional Neural Networks: A Core-Set Approach”, 2017-08-01 (; backlinks; similar)
“Learning to Infer Graphics Programs from Hand-Drawn Images”, Et Al 2017
“Learning to Infer Graphics Programs from Hand-Drawn Images”, 2017-07-30 (; similar)
“Learning Transferable Architectures for Scalable Image Recognition”, Et Al 2017
“Learning Transferable Architectures for Scalable Image Recognition”, 2017-07-21 (; similar)
“Efficient Architecture Search by Network Transformation”, Et Al 2017
“Efficient Architecture Search by Network Transformation”, 2017-07-16 (; backlinks; similar)
“A Simple Neural Attentive Meta-Learner”, Et Al 2017
“A Simple Neural Attentive Meta-Learner”, 2017-07-11 (; backlinks; similar)
“Towards Deep Learning Models Resistant to Adversarial Attacks”, Et Al 2017
“Towards Deep Learning Models Resistant to Adversarial Attacks”, 2017-06-19 (; backlinks; similar; bibliography)
“A Simple Neural Network Module for Relational Reasoning”, Et Al 2017
“A simple neural network module for relational reasoning”, 2017-06-05 (; similar; bibliography)
“What Makes a Good Image? Airbnb Demand Analytics Leveraging Interpretable Image Features”, Et Al 2017
“What Makes a Good Image? Airbnb Demand Analytics Leveraging Interpretable Image Features”, 2017-05-25 (; similar; bibliography)
“Picasso: A Modular Framework for Visualizing the Learning Process of Neural Network Image Classifiers”, 2017
“Picasso: A Modular Framework for Visualizing the Learning Process of Neural Network Image Classifiers”, 2017-05-16 (backlinks; similar)
“Adversarial Neural Machine Translation”, Et Al 2017
“Adversarial Neural Machine Translation”, 2017-04-20 (; backlinks; similar)
“Multi-Scale Dense Networks for Resource Efficient Image Classification”, Et Al 2017
“Multi-Scale Dense Networks for Resource Efficient Image Classification”, 2017-03-29 (backlinks; similar)
“Mask R-CNN”, Et Al 2017
“Mask R-CNN”, 2017-03-20 (similar; bibliography)
“Using Human Brain Activity to Guide Machine Learning”, Et Al 2017
“Using Human Brain Activity to Guide Machine Learning”, 2017-03-16 (; similar)
“Learned Optimizers That Scale and Generalize”, Et Al 2017
“Learned Optimizers that Scale and Generalize”, 2017-03-14 (; backlinks; similar)
“Prediction and Control With Temporal Segment Models”, Et Al 2017
“Prediction and Control with Temporal Segment Models”, 2017-03-12 (; similar)
“Parallel Multiscale Autoregressive Density Estimation”, Et Al 2017
“Parallel Multiscale Autoregressive Density Estimation”, 2017-03-10 (; similar)
“Convolution Aware Initialization”, 2017
“Convolution Aware Initialization”, 2017-02-21 (backlinks; similar)
“Gender-From-Iris or Gender-From-Mascara?”, Et Al 2017
“Gender-From-Iris or Gender-From-Mascara?”, 2017-02-04 (; backlinks; similar)
“BrainNetCNN: Convolutional Neural Networks for Brain Networks; towards Predicting Neurodevelopment”, Et Al 2017
“BrainNetCNN: Convolutional neural networks for brain networks; towards predicting neurodevelopment”, 2017-02 (; backlinks; similar; bibliography)
“PixelCNN++: Improving the PixelCNN With Discretized Logistic Mixture Likelihood and Other Modifications”, Et Al 2017
“PixelCNN++: Improving the PixelCNN with Discretized Logistic Mixture Likelihood and Other Modifications”, 2017-01-19 (similar)
“YOLO9000: Better, Faster, Stronger”, 2016
“YOLO9000: Better, Faster, Stronger”, 2016-12-25 (backlinks; similar; bibliography)
“Language Modeling With Gated Convolutional Networks”, Et Al 2016
“Language Modeling with Gated Convolutional Networks”, 2016-12-23 (similar)
“LipNet: End-to-End Sentence-level Lipreading”, Et Al 2016
“LipNet: End-to-End Sentence-level Lipreading”, 2016-12-16 (; backlinks; similar)
“Understanding Deep Learning Requires Rethinking Generalization”, Et Al 2016
“Understanding deep learning requires rethinking generalization”, 2016-11-10 (; similar)
“Designing Neural Network Architectures Using Reinforcement Learning”, Et Al 2016
“Designing Neural Network Architectures using Reinforcement Learning”, 2016-11-07 (; backlinks; similar)
“HyperNetworks”, Et Al 2016
“HyperNetworks”, 2016-09-27 (; similar)
“Neural Photo Editing With Introspective Adversarial Networks”, Et Al 2016
“Neural Photo Editing with Introspective Adversarial Networks”, 2016-09-22 (; backlinks; similar)
“Direct Feedback Alignment Provides Learning in Deep Neural Networks”, 2016
“Direct Feedback Alignment Provides Learning in Deep Neural Networks”, 2016-09-06 (; backlinks; similar)
“Deep Learning Human Mind for Automated Visual Classification”, Et Al 2016
“Deep Learning Human Mind for Automated Visual Classification”, 2016-09-01 (; similar)
“DenseNet: Densely Connected Convolutional Networks”, Et Al 2016
“DenseNet: Densely Connected Convolutional Networks”, 2016-08-25 (backlinks; similar)
“Deep Learning the City: Quantifying Urban Perception At A Global Scale”, Et Al 2016
“Deep Learning the City: Quantifying Urban Perception At A Global Scale”, 2016-08-05 (; backlinks; similar)
“Convolutional Neural Fabrics”, 2016
“Convolutional Neural Fabrics”, 2016-06-08 (similar)
“FractalNet: Ultra-Deep Neural Networks without Residuals”, Et Al 2016
“FractalNet: Ultra-Deep Neural Networks without Residuals”, 2016-05-24 (similar)
“ViZDoom: A Doom-based AI Research Platform for Visual Reinforcement Learning”, Et Al 2016
“ViZDoom: A Doom-based AI Research Platform for Visual Reinforcement Learning”, 2016-05-06 (; backlinks; similar)
“OHEM: Training Region-based Object Detectors With Online Hard Example Mining”, Et Al 2016
“OHEM: Training Region-based Object Detectors with Online Hard Example Mining”, 2016-04-12 (; similar)
“Deep Networks With Stochastic Depth”, Et Al 2016
“Deep Networks with Stochastic Depth”, 2016-03-30 (similar)
“Convolutional Networks for Fast, Energy-Efficient Neuromorphic Computing”, Et Al 2016
“Convolutional Networks for Fast, Energy-Efficient Neuromorphic Computing”, 2016-03-28 (; similar)
“Do Deep Convolutional Nets Really Need to Be Deep and Convolutional?”, Et Al 2016
“Do Deep Convolutional Nets Really Need to be Deep and Convolutional?”, 2016-03-17 (; backlinks; similar)
“Learning Hand-Eye Coordination for Robotic Grasping With Deep Learning and Large-Scale Data Collection”, Et Al 2016
“Learning Hand-Eye Coordination for Robotic Grasping with Deep Learning and Large-Scale Data Collection”, 2016-03-07 (; similar)
“Network Morphism”, Et Al 2016
“Network Morphism”, 2016-03-05 (; similar)
“Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning”, Et Al 2016
“Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning”, 2016-02-23 (similar)
“PlaNet—Photo Geolocation With Convolutional Neural Networks”, Et Al 2016
“PlaNet—Photo Geolocation with Convolutional Neural Networks”, 2016-02-17 (; similar)
“Value Iteration Networks”, Et Al 2016
“Value Iteration Networks”, 2016-02-09 (; similar)
“Pixel Recurrent Neural Networks”, Et Al 2016
“Pixel Recurrent Neural Networks”, 2016-01-25 (; similar)
“Image Synthesis from Yahoo’s open_nsfw”, 2016
“Image Synthesis from Yahoo’s open_nsfw”, 2016 (backlinks; similar)
“Deep Residual Learning for Image Recognition”, Et Al 2015
“Deep Residual Learning for Image Recognition”, 2015-12-10 (similar)
“Adding Gradient Noise Improves Learning for Very Deep Networks”, Et Al 2015
“Adding Gradient Noise Improves Learning for Very Deep Networks”, 2015-11-21 (; similar)
“Learning Visual Features from Large Weakly Supervised Data”, Et Al 2015
“Learning Visual Features from Large Weakly Supervised Data”, 2015-11-06 (; similar; bibliography)
“Predicting and Understanding Urban Perception With Convolutional Neural Networks”, Et Al 2015
“Predicting and Understanding Urban Perception with Convolutional Neural Networks”, 2015-10-01 (; backlinks; similar; bibliography)
“LSUN: Construction of a Large-scale Image Dataset Using Deep Learning With Humans in the Loop”, Et Al 2015
“LSUN: Construction of a Large-scale Image Dataset using Deep Learning with Humans in the Loop”, 2015-06-10 (; backlinks; similar)
“You Only Look Once: Unified, Real-Time Object Detection”, Et Al 2015
“You Only Look Once: Unified, Real-Time Object Detection”, 2015-06-08 (backlinks; similar; bibliography)
“Clothing-1M: Learning from Massive Noisy Labeled Data for Image Classification”, Et Al 2015
“Clothing-1M: Learning from Massive Noisy Labeled Data for Image Classification”, 2015-06-07 (; similar; bibliography)
“Faster R-CNN: Towards Real-Time Object Detection With Region Proposal Networks”, Et Al 2015
“Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks”, 2015-06-04 (similar; bibliography)
“Cyclical Learning Rates for Training Neural Networks”, 2015
“Cyclical Learning Rates for Training Neural Networks”, 2015-06-03 (backlinks; similar; bibliography)
“Deep Learning”, Et Al 2015
“Deep Learning”, 2015-05-28 (similar)
“Fast R-CNN”, 2015
“Fast R-CNN”, 2015-04-30 (similar; bibliography)
“End-to-End Training of Deep Visuomotor Policies”, Et Al 2015
“End-to-End Training of Deep Visuomotor Policies”, 2015-04-02 (; similar)
“FaceNet: A Unified Embedding for Face Recognition and Clustering”, Et Al 2015
“FaceNet: A Unified Embedding for Face Recognition and Clustering”, 2015-03-12 (similar)
“DeepID3: Face Recognition With Very Deep Neural Networks”, Et Al 2015
“DeepID3: Face Recognition with Very Deep Neural Networks”, 2015-02-03 (similar)
“Understanding Image Representations by Measuring Their Equivariance and Equivalence”, 2014
“Understanding image representations by measuring their equivariance and equivalence”, 2014-11-21 (backlinks; similar)
“Going Deeper With Convolutions”, Et Al 2014
“Going Deeper with Convolutions”, 2014-09-17 (similar)
“Very Deep Convolutional Networks for Large-Scale Image Recognition”, 2014
“Very Deep Convolutional Networks for Large-Scale Image Recognition”, 2014-09-04 (backlinks; similar)
“ImageNet Large Scale Visual Recognition Challenge”, Et Al 2014
“ImageNet Large Scale Visual Recognition Challenge”, 2014-09-01 (; backlinks; similar)
“Deep Learning Face Representation by Joint Identification-Verification”, Et Al 2014
“Deep Learning Face Representation by Joint Identification-Verification”, 2014-06-18 (backlinks; similar)
“ImageNet Classification With Deep Convolutional Neural Networks”, Et Al 2012
“ImageNet Classification with Deep Convolutional Neural Networks”, 2012-12 (backlinks; similar)
“Multi-column Deep Neural Network for Traffic Sign Classification”, Cireşan Et Al 2012
“Multi-column deep neural network for traffic sign classification”, 2012-08 (; backlinks; similar)
“Multi-column Deep Neural Networks for Image Classification”, Cireşan Et Al 2012
“Multi-column Deep Neural Networks for Image Classification”, 2012-02-13 (; similar)
“DanNet: Flexible, High Performance Convolutional Neural Networks for Image Classification”, Et Al 2011
“DanNet: Flexible, High Performance Convolutional Neural Networks for Image Classification”, 2011-02-01 (; similar; bibliography)
Wikipedia
Miscellaneous
Link Bibliography
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https://arxiv.org/abs/2212.06727: “What Do Vision Transformers Learn? A Visual Exploration”, : -
2022-carragher.pdf: “Simulated Automated Facial Recognition Systems As Decision-aids in Forensic Face Matching Tasks”, Daniel J. Carragher, Peter J. B. Hancock: -
2022-lan.pdf: “GCN-Based Multi-Modal Multi-Label Attribute Classification in Anime Illustration Using Domain-Specific Semantic Features”, Ziwen Lan, Keisuke Maeda, Takahiro Ogawa, Miki Haseyama: -
https://arxiv.org/abs/2210.03651: “Understanding the Covariance Structure of Convolutional Filters”, Asher Trockman, Devin Willmott, J. Zico Kolter: -
2022-pototzky.pdf: “FastSiam: Resource-Efficient Self-supervised Learning on a Single GPU”, Daniel Pototzky, Azhar Sultan, Lars Schmidt-Thieme: -
https://www.science.org/doi/10.1126/sciadv.abm2219: “Reassessing Hierarchical Correspondences between Brain and Deep Networks through Direct Interface”, Nicholas J. Sexton, Bradley C. Love: -
https://arxiv.org/abs/2206.07137: “RHO-LOSS: Prioritized Training on Points That Are Learnable, Worth Learning, and Not Yet Learnt”, : -
https://arxiv.org/abs/2206.04658#nvidia: “BigVGAN: A Universal Neural Vocoder With Large-Scale Training”, Sang-gil Lee, Wei Ping, Boris Ginsburg, Bryan Catanzaro, Sungroh Yoon: -
https://arxiv.org/abs/2203.06717: “Scaling Up Your Kernels to 31×31: Revisiting Large Kernel Design in CNNs (RepLKNet)”, Xiaohan Ding, Xiangyu Zhang, Yizhuang Zhou, Jungong Han, Guiguang Ding, Jian Sun: -
https://openreview.net/forum?id=SkfMWhAqYQ: “Approximating CNNs With Bag-of-local-Features Models Works Surprisingly Well on ImageNet”, Wiel, Brendel, Matthias Bethge: -
https://arxiv.org/abs/2201.09792: “ConvMixer: Patches Are All You Need?”, Asher Trockman, J. Zico Kolter: -
https://arxiv.org/abs/2201.03545#facebook: “ConvNeXt: A ConvNet for the 2020s”, Zhuang Liu, Hanzi Mao, Chao-Yuan Wu, Christoph Feichtenhofer, Trevor Darrell, Saining Xie: -
https://arxiv.org/abs/2110.06848: “Decoupled Contrastive Learning”, Chun-Hsiao Yeh, Cheng-Yao Hong, Yen-Chi Hsu, Tyng-Luh Liu, Yubei Chen, Yann LeCun: -
https://arxiv.org/abs/2110.05208: “Supervision Exists Everywhere: A Data Efficient Contrastive Language-Image Pre-training Paradigm (DeCLIP)”, Yangguang Li, Feng Liang, Lichen Zhao, Yufeng Cui, Wanli Ouyang, Jing Shao, Fengwei Yu, Junjie Yan: -
https://www.frontiersin.org/articles/10.3389/fninf.2021.679838/full: “<code>THINGSvision< / code>: A Python Toolbox for Streamlining the Extraction of Activations From Deep Neural Networks”, Lukas Muttenthaler, Martin N. Hebart: -
https://arxiv.org/abs/2106.07411: “Partial Success in Closing the Gap between Human and Machine Vision”, : -
https://arxiv.org/abs/2106.04803#google: “CoAtNet: Marrying Convolution and Attention for All Data Sizes”, Zihang Dai, Hanxiao Liu, Quoc V. Le, Mingxing Tan: -
https://arxiv.org/abs/2106.00116: “Effect of Pre-Training Scale on Intra / Inter-Domain Full and Few-Shot Transfer Learning for Natural and Medical X-Ray Chest Images”, Mehdi Cherti, Jenia Jitsev: -
https://arxiv.org/abs/2104.05623: “Rethinking and Improving the Robustness of Image Style Transfer”, Pei Wang, Yijun Li, Nuno Vasconcelos: -
https://arxiv.org/abs/2104.00613: “The Surprising Impact of Mask-head Architecture on Novel Class Segmentation”, Vighnesh Birodkar, Zhichao Lu, Siyang Li, Vivek Rathod, Jonathan Huang: -
https://arxiv.org/abs/2103.14749: “Pervasive Label Errors in Test Sets Destabilize Machine Learning Benchmarks”, Curtis G. Northcutt, Anish Athalye, Jonas Mueller: -
https://ai.facebook.com/blog/learning-from-videos-to-understand-the-world/: “Learning from Videos to Understand the World”, Geoffrey Zweig, Polina Kuznetsova, Michael Auli, Francois Fagan: -
https://arxiv.org/abs/2102.06171#deepmind: “NFNet: High-Performance Large-Scale Image Recognition Without Normalization”, Andrew Brock, Soham De, Samuel L. Smith, Karen Simonyan: -
https://arxiv.org/abs/2003.10580#google: “Meta Pseudo Labels”, Hieu Pham, Zihang Dai, Qizhe Xie, Minh-Thang Luong, Quoc V. Le: -
https://greydanus.github.io/2020/12/01/scaling-down/: “Scaling down Deep Learning”, Sam Greydanus: -
https://arxiv.org/abs/2011.10650#openai: “Very Deep VAEs Generalize Autoregressive Models and Can Outperform Them on Images”, Rewon Child: -
https://www.ethanrosenthal.com/2020/08/25/optimal-peanut-butter-and-banana-sandwiches/: “Optimal Peanut Butter and Banana Sandwiches”, Ethan Rosenthal: -
https://arxiv.org/abs/2008.09037: “Accuracy and Performance Comparison of Video Action Recognition Approaches”, : -
https://arxiv.org/abs/2007.03898#nvidia: “NVAE: A Deep Hierarchical Variational Autoencoder”, Arash Vahdat, Jan Kautz: -
https://arxiv.org/abs/2004.10934: “YOLOv4: Optimal Speed and Accuracy of Object Detection”, Alexey Bochkovskiy, Chien-Yao Wang, Hong-Yuan Mark Liao: -
https://arxiv.org/abs/2004.10802: “Scaling Laws from the Data Manifold Dimension”, Utkarsh Sharma, Jared Kaplan: -
https://arxiv.org/abs/2002.05709#google: “A Simple Framework for Contrastive Learning of Visual Representations”, Ting Chen, Simon Kornblith, Mohammad Norouzi, Geoffrey Hinton: -
https://arxiv.org/abs/2002.03629: “Accelerating Feedforward Computation via Parallel Nonlinear Equation Solving”, Yang Song, Chenlin Meng, Renjie Liao, Stefano Ermon: -
https://openai.com/blog/deep-double-descent/: “Deep Double Descent: We Show That the Double Descent Phenomenon Occurs in CNNs, ResNets, and Transformers: Performance First Improves, Then Gets Worse, and Then Improves Again With Increasing Model Size, Data Size, or Training Time. This Effect Is Often Avoided through Careful Regularization. While This Behavior Appears to Be Fairly Universal, We Don’t yet Fully Understand Why It Happens, and View Further Study of This Phenomenon As an Important Research Direction.”, Preetum Nakkiran, Gal Kaplun, Yamini Bansal, Tristan Yang, Boaz Barak, Ilya Sutskever: -
https://arxiv.org/abs/1911.00357#facebook: “DD-PPO: Learning Near-Perfect PointGoal Navigators from 2.5 Billion Frames”, Erik Wijmans, Abhishek Kadian, Ari Morcos, Stefan Lee, Irfan Essa, Devi Parikh, Manolis Savva, Dhruv Batra: -
2019-jaderberg.pdf#deepmind: “Human-level Performance in 3D Multiplayer Games With Population-based Reinforcement Learning”, : -
https://arxiv.org/abs/1905.04899: “CutMix: Regularization Strategy to Train Strong Classifiers With Localizable Features”, Sangdoo Yun, Dongyoon Han, Seong Joon Oh, Sanghyuk Chun, Junsuk Choe, Youngjoon Yoo: -
https://arxiv.org/abs/1905.00546#facebook: “Billion-scale Semi-supervised Learning for Image Classification”, I. Zeki Yalniz, Hervé Jégou, Kan Chen, Manohar Paluri, Dhruv Mahajan: -
2018-fu.pdf: “StreetNet: Preference Learning With Convolutional Neural Network on Urban Crime Perception”, Kaiqun Fu, Zhiqian Chen, Chang-Tien Lu: -
https://arxiv.org/abs/1808.01097: “CurriculumNet: Weakly Supervised Learning from Large-Scale Web Images”, Sheng Guo, Weilin Huang, Haozhi Zhang, Chenfan Zhuang, Dengke Dong, Matthew R. Scott, Dinglong Huang: -
https://arxiv.org/abs/1805.00932#facebook: “Exploring the Limits of Weakly Supervised Pretraining”, : -
https://arxiv.org/abs/1804.02767: “YOLOv3: An Incremental Improvement”, Joseph Redmon, Ali Farhadi: -
2018-choi.pdf: “Guess, Check and Fix: a Phenomenology of Improvisation in ‘neural’ Painting”, Suk Kyoung Choi: -
2017-sabatelli.pdf#page=3: “Learning to Play Chess With Minimal Lookahead and Deep Value Neural Networks”, Matthia Sabatelli: -
https://arxiv.org/abs/1708.05344: “SMASH: One-Shot Model Architecture Search through HyperNetworks”, Andrew Brock, Theodore Lim, J. M. Ritchie, Nick Weston: -
https://arxiv.org/abs/1706.06083: “Towards Deep Learning Models Resistant to Adversarial Attacks”, Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, Adrian Vladu: -
https://arxiv.org/abs/1706.01427#deepmind: “A Simple Neural Network Module for Relational Reasoning”, : -
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2976021: “What Makes a Good Image? Airbnb Demand Analytics Leveraging Interpretable Image Features”, Shunyuan Zhang, Dokyun Lee, Param Vir Singh, Kannan Srinivasan: -
https://arxiv.org/abs/1703.06870#facebook: “Mask R-CNN”, Kaiming He, Georgia Gkioxari, Piotr Dollár, Ross Girshick: -
2017-kawahara.pdf: “BrainNetCNN: Convolutional Neural Networks for Brain Networks; towards Predicting Neurodevelopment”, : -
https://arxiv.org/abs/1612.08242: “YOLO9000: Better, Faster, Stronger”, Joseph Redmon, Ali Farhadi: -
https://arxiv.org/abs/1511.02251#facebook: “Learning Visual Features from Large Weakly Supervised Data”, Arm, Joulin, Laurens van der Maaten, Allan Jabri, Nicolas Vasilache: -
2015-porzi.pdf: “Predicting and Understanding Urban Perception With Convolutional Neural Networks”, Lorenzo Porzi, Samuel Rota Bulò, Bruno Lepri, Elisa Ricci: -
https://arxiv.org/abs/1506.02640: “You Only Look Once: Unified, Real-Time Object Detection”, Joseph Redmon, Santosh Divvala, Ross Girshick, Ali Farhadi: -
https://openaccess.thecvf.com/content_cvpr_2015/papers/Xiao_Learning_From_Massive_2015_CVPR_paper.pdf#baidu: “Clothing-1M: Learning from Massive Noisy Labeled Data for Image Classification”, Tong Xiao, Tian Xia, Yi Yang, Chang Huang, Xiaogang Wang: -
https://arxiv.org/abs/1506.01497#microsoft: “Faster R-CNN: Towards Real-Time Object Detection With Region Proposal Networks”, Shaoqing Ren, Kaiming He, Ross Girshick, Jian Sun: -
https://arxiv.org/abs/1506.01186: “Cyclical Learning Rates for Training Neural Networks”, Leslie N. Smith: -
https://arxiv.org/abs/1504.08083#microsoft: “Fast R-CNN”, Ross Girshick: -
https://arxiv.org/abs/1102.0183#schmidhuber: “DanNet: Flexible, High Performance Convolutional Neural Networks for Image Classification”, Dan Claudiu Ciresan, Ueli Meier, Jonathan Masci, Luca Maria Gambardella, Jürgen Schmidhuber: