“Vector Quantized Image-to-Image Translation”, Chen Et Al 2022

“Vector Quantized Image-to-Image Translation”⁠, Yu-Jie Chen, Shin-I Cheng, Wei-Chen Chiu, Hung-Yu Tseng, Hsin-Ying Lee2022-07-27 (DALL·E; similar)

“Draft-and-Revise: Effective Image Generation With Contextual RQ-Transformer”, Lee Et Al 2022

“Draft-and-Revise: Effective Image Generation with Contextual RQ-Transformer”⁠, Doyup Lee, Chiheon Kim, Saehoon Kim, Minsu Cho, Wook-Shin Han2022-06-09 (DALL·E; similar)

“Closing the Gap: Exact Maximum Likelihood Training of Generative Autoencoders Using Invertible Layers (AEF)”, Silvestri Et Al 2022

“Closing the gap: Exact maximum likelihood training of generative autoencoders using invertible layers (AEF)”⁠, Gianluigi Silvestri, Daan Roos, Luca Ambrogioni2022-05-19 (similar)

“AvatarCLIP: Zero-Shot Text-Driven Generation and Animation of 3D Avatars”, Hong Et Al 2022

“AvatarCLIP: Zero-Shot Text-Driven Generation and Animation of 3D Avatars”⁠, Fangzhou Hong, Mingyuan Zhang, Liang Pan, Zhongang Cai, Lei Yang, Ziwei Liu2022-05-17 (CLIP; similar; bibliography)

“NaturalSpeech: End-to-End Text to Speech Synthesis With Human-Level Quality”, Tan Et Al 2022

“NaturalSpeech: End-to-End Text to Speech Synthesis with Human-Level Quality”⁠, Xu Tan, Jiawei Chen, Haohe Liu, Jian Cong, Chen Zhang, Yanqing Liu, Xi Wang, Yichong Leng, Yuanhao Yi2022-05-09 (similar; bibliography)

“VQGAN-CLIP: Open Domain Image Generation and Editing With Natural Language Guidance”, Crowson Et Al 2022

“VQGAN-CLIP: Open Domain Image Generation and Editing with Natural Language Guidance”⁠, Katherine Crowson, Stella Biderman, Daniel Kornis, Dashiell Stander, Eric Hallahan, Louis Castricato2022-04-18 (GAN⁠, CLIP; similar)

“TATS: Long Video Generation With Time-Agnostic VQGAN and Time-Sensitive Transformer”, Ge Et Al 2022

“TATS: Long Video Generation with Time-Agnostic VQGAN and Time-Sensitive Transformer”⁠, Songwei Ge, Thomas Hayes, Harry Yang, Xi Yin, Guan Pang, David Jacobs, Jia-Bin Huang, Devi Parikh2022-04-07 (GAN⁠, self-attention⁠, video generation; similar; bibliography)

“Diffusion Probabilistic Modeling for Video Generation”, Yang Et Al 2022

“Diffusion Probabilistic Modeling for Video Generation”⁠, Ruihan Yang, Prakhar Srivastava, Stephan Mandt2022-03-16 (diffusion model⁠, video generation; similar)

“Polarity Sampling: Quality and Diversity Control of Pre-Trained Generative Networks via Singular Values”, Humayun Et Al 2022

“Polarity Sampling: Quality and Diversity Control of Pre-Trained Generative Networks via Singular Values”⁠, Ahmed Imtiaz Humayun, Randall Balestriero, Richard Baraniuk2022-03-03 (BigGAN⁠, StyleGAN; similar; bibliography)

“Variational Autoencoders Without the Variation”, Daly Et Al 2022

“Variational Autoencoders Without the Variation”⁠, Gregory A. Daly, Jonathan E. Fieldsend, Gavin Tabor2022-03-01 (CNN⁠, AI scaling; similar)

“Vector-quantized Image Modeling With Improved VQGAN”, Yu Et Al 2022

“Vector-quantized Image Modeling with Improved VQGAN”⁠, Jiahui Yu, Xin Li, Jing Yu Koh, Han Zhang, Ruoming Pang, James Qin, Alexander Ku, Yuanzhong Xu, Jason Baldridge2022-03-01 (GAN⁠, GPT; similar; bibliography)

“MLR: A Model of Working Memory for Latent Representations”, Hedayati Et Al 2022

“MLR: A model of working memory for latent representations”⁠, Shekoofeh Hedayati, Ryan O′Donnell, Brad Wyble2022-02-03 (DNB⁠, neuroscience; similar)

“CM3: A Causal Masked Multimodal Model of the Internet”, Aghajanyan Et Al 2022

“CM3: A Causal Masked Multimodal Model of the Internet”⁠, Armen Aghajanyan, Bernie Huang, Candace Ross, Vladimir Karpukhin, Hu Xu, Naman Goyal, Dmytro Okhonko2022-01-19 (CLIP⁠, DALL·E⁠, AI scaling; similar)

“Design Guidelines for Prompt Engineering Text-to-Image Generative Models”, Liu & Chilton2022

“Design Guidelines for Prompt Engineering Text-to-Image Generative Models”⁠, Vivian Liu, Lydia B. Chilton2022-01-07 (CLIP; similar; bibliography)

“DiffuseVAE: Efficient, Controllable and High-Fidelity Generation from Low-Dimensional Latents”, Pandey Et Al 2022

“DiffuseVAE: Efficient, Controllable and High-Fidelity Generation from Low-Dimensional Latents”⁠, Kushagra Pandey, Avideep Mukherjee, Piyush Rai, Abhishek Kumar2022-01-02 (diffusion model; similar)

“ERNIE-ViLG: Unified Generative Pre-training for Bidirectional Vision-Language Generation”, Zhang Et Al 2021

“ERNIE-ViLG: Unified Generative Pre-training for Bidirectional Vision-Language Generation”⁠, Han Zhang, Weichong Yin, Yewei Fang, Lanxin Li, Boqiang Duan, Zhihua Wu, Yu Sun, Hao Tian, Hua Wu, Haifeng Wang2021-12-31 (ML dataset⁠, DALL·E; similar; bibliography)

“Discovering State Variables Hidden in Experimental Data”, Chen Et Al 2021

“Discovering State Variables Hidden in Experimental Data”⁠, Boyuan Chen, Kuang Huang, Sun, Raghupathi, Ishaan Chandratreya, Qiang Du, Hod Lipson2021-12-20 (similar)

“High-Resolution Image Synthesis With Latent Diffusion Models”, Rombach Et Al 2021

“High-Resolution Image Synthesis with Latent Diffusion Models”⁠, Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, Björn Ommer2021-12-20 (diffusion model⁠, CLIP; backlinks; similar; bibliography)

“VQ-DDM: Global Context With Discrete Diffusion in Vector Quantized Modelling for Image Generation”, Hu Et Al 2021

“VQ-DDM: Global Context with Discrete Diffusion in Vector Quantized Modelling for Image Generation”⁠, Minghui Hu, Yujie Wang, Tat-Jen Cham, Jianfei Yang, P. N. Suganthan2021-12-03 (diffusion model; similar)

“Vector Quantized Diffusion Model for Text-to-Image Synthesis”, Gu Et Al 2021

“Vector Quantized Diffusion Model for Text-to-Image Synthesis”⁠, Shuyang Gu, Dong Chen, Jianmin Bao, Fang Wen, Bo Zhang, Dongdong Chen, Lu Yuan, Baining Guo2021-11-29 (diffusion model; similar)

“L-Verse: Bidirectional Generation Between Image and Text”, Kim Et Al 2021

“L-Verse: Bidirectional Generation Between Image and Text”⁠, Taehoon Kim, Gwangmo Song, Sihaeng Lee, Sangyun Kim, Yewon Seo, Soonyoung Lee, Seung Hwan Kim, Honglak Lee2021-11-22 (DALL·E⁠, AI scaling; backlinks; similar; bibliography)

“Passive Non-Line-of-Sight Imaging Using Optimal Transport”, Geng Et Al 2021

“Passive Non-Line-of-Sight Imaging Using Optimal Transport”⁠, Ruixu Geng, Yang Hu, Zhi Lu, Cong Yu, Houqiang Li, Hengyu Zhang, Yan Chen2021-11-22 (Danbooru AI; similar)

“Unsupervised Deep Learning Identifies Semantic Disentanglement in Single Inferotemporal Face Patch Neurons”, Higgins Et Al 2021

“Unsupervised deep learning identifies semantic disentanglement in single inferotemporal face patch neurons”⁠, Irina Higgins, Le Chang, Victoria Langston, Demis Hassabis, Christopher Summerfield, Doris Tsao, Matthew Botvinick2021-11-09 (neuroscience⁠, RL/brain-imitation-learning; similar)

“Telling Creative Stories Using Generative Visual Aids”, Ali & Parikh2021

“Telling Creative Stories Using Generative Visual Aids”⁠, Safinah Ali, Devi Parikh2021-10-27 (BigGAN⁠, CLIP⁠, DALL·E⁠, GPT fiction; similar)

“Illiterate DALL·E Learns to Compose”, Singh Et Al 2021

“Illiterate DALL·E Learns to Compose”⁠, Gautam Singh, Fei Deng, Sungjin Ahn2021-10-17 (DALL·E; similar)

“Score-based Generative Modeling in Latent Space”, Vahdat Et Al 2021

“Score-based Generative Modeling in Latent Space”⁠, Arash Vahdat, Karsten Kreis, Jan Kautz2021-06-10 (diffusion model; similar)

“Vector Quantized Models for Planning”, Ozair Et Al 2021

“Vector Quantized Models for Planning”⁠, Sherjil Ozair, Yazhe Li, Ali Razavi, Ioannis Antonoglou, Aäron van den Oord, Oriol Vinyals2021-06-08 (video generation⁠, AI chess⁠, MuZero; similar)

“NWT: Towards Natural Audio-to-video Generation With Representation Learning”, Mama Et Al 2021

“NWT: Towards natural audio-to-video generation with representation learning”⁠, Rayhane Mama, Marc S. Tyndel, Hashiam Kadhim, Cole Clifford, Ragavan Thurairatnam2021-06-08 (video generation; similar)

“VideoGPT: Video Generation Using VQ-VAE and Transformers”, Yan Et Al 2021

“VideoGPT: Video Generation using VQ-VAE and Transformers”⁠, Wilson Yan, Yunzhi Zhang, Pieter Abbeel, Aravind Srinivas2021-04-20 (DALL·E⁠, Jukebox⁠, video generation; backlinks; similar; bibliography)

“Symbolic Music Generation With Diffusion Models”, Mittal Et Al 2021

“Symbolic Music Generation with Diffusion Models”⁠, Gautam Mittal, Jesse Engel, Curtis Hawthorne, Ian Simon2021-03-30 (AI/music⁠, diffusion model/discrete; similar)

“Deep Generative Modelling: A Comparative Review of VAEs, GANs, Normalizing Flows, Energy-Based and Autoregressive Models”, Bond-Taylor Et Al 2021

“Deep Generative Modelling: A Comparative Review of VAEs, GANs, Normalizing Flows, Energy-Based and Autoregressive Models”⁠, Sam Bond-Taylor, Adam Leach, Yang Long, Chris G. Willcocks2021-03-08 (GAN; backlinks; similar)

“Greedy Hierarchical Variational Autoencoders (GHVAEs) for Large-Scale Video Prediction”, Wu Et Al 2021

“Greedy Hierarchical Variational Autoencoders (GHVAEs) for Large-Scale Video Prediction”⁠, Bohan Wu, Suraj Nair, Roberto Martin-Martin, Li Fei-Fei, Chelsea Finn2021-03-06 (AI scaling⁠, video generation; similar)

“CW-VAE: Clockwork Variational Autoencoders”, Saxena Et Al 2021

“CW-VAE: Clockwork Variational Autoencoders”⁠, Vaibhav Saxena, Jimmy Ba, Danijar Hafner2021-02-18 (video generation; similar)

“Denoising Diffusion Implicit Models”, Song Et Al 2021

“Denoising Diffusion Implicit Models”⁠, Jiaming Song, Chenlin Meng, Stefano Ermon2021-01-25 (diffusion model; similar)

“DALL·E 1: Creating Images from Text: We’ve Trained a Neural Network Called DALL·E That Creates Images from Text Captions for a Wide Range of Concepts Expressible in Natural Language”, Ramesh Et Al 2021

“DALL·E 1: Creating Images from Text: We’ve trained a neural network called DALL·E that creates images from text captions for a wide range of concepts expressible in natural language”⁠, Aditya Ramesh, Mikhail Pavlov, Gabriel Goh, Scott Gray, Mark Chen, Rewon Child, Vedant Misra, Pamela Mishkin2021-01-05 (CLIP⁠, DALL·E; backlinks; similar; bibliography)

“VQ-GAN: Taming Transformers for High-Resolution Image Synthesis”, Esser Et Al 2020

“VQ-GAN: Taming Transformers for High-Resolution Image Synthesis”⁠, Patrick Esser, Robin Rombach, Björn Ommer2020-12-17 (GAN⁠, Transformer NN; backlinks; similar)

“Very Deep VAEs Generalize Autoregressive Models and Can Outperform Them on Images”, Child2020

“Very Deep VAEs Generalize Autoregressive Models and Can Outperform Them on Images”⁠, Rewon Child2020-11-20 (CNN⁠, AI scaling; similar; bibliography)

“NVAE: A Deep Hierarchical Variational Autoencoder”, Vahdat & Kautz2020

“NVAE: A Deep Hierarchical Variational Autoencoder”⁠, Arash Vahdat, Jan Kautz2020-07-08 (CNN⁠, AI scaling; similar; bibliography)

“Jukebox: We’re Introducing Jukebox, a Neural Net That Generates Music, including Rudimentary Singing, As Raw Audio in a Variety of Genres and Artist Styles. We’re Releasing the Model Weights and Code, along With a Tool to Explore the Generated Samples.”, Dhariwal Et Al 2020

“Jukebox: We’re introducing Jukebox, a neural net that generates music, including rudimentary singing, as raw audio in a variety of genres and artist styles. We’re releasing the model weights and code, along with a tool to explore the generated samples.”⁠, Prafulla Dhariwal, Heewoo Jun, Christine Payne, Jong Wook Kim, Alec Radford, Ilya Sutskever2020-04-30 (Jukebox⁠, AI scaling; backlinks; similar; bibliography)

“Jukebox: A Generative Model for Music”, Dhariwal Et Al 2020

“Jukebox: A Generative Model for Music”⁠, Prafulla Dhariwal, Heewoo Jun, Christine Payne, Jong Wook Kim, Alec Radford, Ilya Sutskever2020-04-30 (Jukebox; backlinks; similar; bibliography)

“RL Agents Implicitly Learning Human Preferences”, Wichers2020

“RL agents Implicitly Learning Human Preferences”⁠, Nevan Wichers2020-02-14 (preference learning; similar)

“Encoding Musical Style With Transformer Autoencoders”, Choi Et Al 2019

“Encoding Musical Style with Transformer Autoencoders”⁠, Kristy Choi, Curtis Hawthorne, Ian Simon, Monica Dinculescu, Jesse Engel2019-12-10 (AI/music⁠, Transformer NN; backlinks; similar)

“Generating Furry Face Art from Sketches Using a GAN”, Yu2019

“Generating Furry Face Art from Sketches using a GAN”⁠, Andrew Yu2019-12-01 (anime AI⁠, GAN; similar)

“BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension”, Lewis Et Al 2019

“BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension”⁠, Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Ves Stoyanov2019-10-29 (similar; bibliography)

“Bayesian Parameter Estimation Using Conditional Variational Autoencoders for Gravitational-wave Astronomy”, Gabbard Et Al 2019

“Bayesian parameter estimation using conditional variational autoencoders for gravitational-wave astronomy”⁠, Hunter Gabbard, Chris Messenger, Ik Siong Heng, Francesco Tonolini, Roderick Murray-Smith2019-09-13 (Bayes; similar)

“In-field Whole Plant Maize Architecture Characterized by Latent Space Phenotyping”, Gage Et Al 2019

“In-field whole plant maize architecture characterized by Latent Space Phenotyping”⁠, Joseph L. Gage, Elliot Richards, Nicholas Lepak, Nicholas Kaczmar, Chinmay Soman, Girish Chowdhary, Michael A. Gore2019-09-10 (heritability⁠, variance components; backlinks; similar)

“Generating Diverse High-Fidelity Images With VQ-VAE-2”, Razavi Et Al 2019

“Generating Diverse High-Fidelity Images with VQ-VAE-2”⁠, Ali Razavi, Aaron van den Oord, Oriol Vinyals2019-06-02 (Jukebox; similar)

“Hierarchical Autoregressive Image Models With Auxiliary Decoders”, Fauw Et Al 2019

“Hierarchical Autoregressive Image Models with Auxiliary Decoders”⁠, Jeffrey De Fauw, Sander Dieleman, Karen Simonyan2019-03-06 (similar)

“Anime Neural Net Graveyard”, Branwen2019

“Anime Neural Net Graveyard”⁠, Gwern Branwen2019-02-04 (Danbooru AI⁠, BigGAN⁠, StyleGAN; backlinks; similar; bibliography)

“How AI Training Scales”, McCandlish Et Al 2018

“How AI Training Scales”⁠, Sam McCandlish, Jared Kaplan, Dario Amodei2018-12-14 (GAN⁠, AI scaling⁠, OA5⁠, RL scaling; backlinks; similar; bibliography)

“An Empirical Model of Large-Batch Training”, McCandlish Et Al 2018

“An Empirical Model of Large-Batch Training”⁠, Sam McCandlish, Jared Kaplan, Dario Amodei, Open A. I. Dota Team2018-12-14 (AI scaling/hardware⁠, OA5⁠, RL scaling; similar)

“Neural Probabilistic Motor Primitives for Humanoid Control”, Merel Et Al 2018

“Neural probabilistic motor primitives for humanoid control”⁠, Josh Merel, Leonard Hasenclever, Alexandre Galashov, Arun Ahuja, Vu Pham, Greg Wayne, Yee Whye Teh, Nicolas Heess2018-11-28 (knowledge distillation⁠, model-free RL⁠, robotics; similar)

“Piano Genie”, Donahue Et Al 2018

“Piano Genie”⁠, Chris Donahue, Ian Simon, Sander Dieleman2018-10-11 (AI/music⁠, RNN; similar)

“IntroVAE: Introspective Variational Autoencoders for Photographic Image Synthesis”, Huang Et Al 2018

“IntroVAE: Introspective Variational Autoencoders for Photographic Image Synthesis”⁠, Huaibo Huang, Zhihang Li, Ran He, Zhenan Sun, Tieniu Tan2018-07-17 (GAN; backlinks; similar)

“InfoNCE: Representation Learning With Contrastive Predictive Coding (CPC)”, Oord Et Al 2018

“InfoNCE: Representation Learning with Contrastive Predictive Coding (CPC)”⁠, Aaron van den Oord, Yazhe Li, Oriol Vinyals2018-07-10 (model-free RL; similar)

“The Challenge of Realistic Music Generation: Modelling Raw Audio at Scale”, Dieleman Et Al 2018

“The challenge of realistic music generation: modelling raw audio at scale”⁠, Sander Dieleman, Aäron van den Oord, Karen Simonyan2018-06-26 (AI/music; similar)

“Self-Net: Lifelong Learning via Continual Self-Modeling”, Camp Et Al 2018

“Self-Net: Lifelong Learning via Continual Self-Modeling”⁠, Blake Camp, Jaya Krishna Mandivarapu, Rolando Estrada2018-05-25 (knowledge distillation; similar)

“XGAN: Unsupervised Image-to-Image Translation for Many-to-Many Mappings”, Royer Et Al 2017

“XGAN: Unsupervised Image-to-Image Translation for Many-to-Many Mappings”⁠, Amélie Royer, Konstantinos Bousmalis, Stephan Gouws, Fred Bertsch, Inbar Mosseri, Forrester Cole, Kevin Murphy2017-11-14 (GAN; similar)

“VQ-VAE: Neural Discrete Representation Learning”, Oord Et Al 2017

“VQ-VAE: Neural Discrete Representation Learning”⁠, Aaron van den Oord, Oriol Vinyals, Koray Kavukcuoglu2017-11-02 (DALL·E; similar)

“Vision-Based Multi-Task Manipulation for Inexpensive Robots Using End-To-End Learning from Demonstration”, Rahmatizadeh Et Al 2017

“Vision-Based Multi-Task Manipulation for Inexpensive Robots Using End-To-End Learning from Demonstration”⁠, Rouhollah Rahmatizadeh, Pooya Abolghasemi, Ladislau Bölöni, Sergey Levine2017-07-10 (GAN⁠, robotics; similar)

“Beta-VAE: Learning Basic Visual Concepts With a Constrained Variational Framework”, Higgins Et Al 2017

“beta-VAE: Learning Basic Visual Concepts with a Constrained Variational Framework”⁠, Irina Higgins, Loic Matthey, Arka Pal, Christopher Burgess, Xavier Glorot, Matthew Botvinick, Shakir Mohamed2017-04-18 (similar)

“Neural Audio Synthesis of Musical Notes With WaveNet Autoencoders”, Engel Et Al 2017

“Neural Audio Synthesis of Musical Notes with WaveNet Autoencoders”⁠, Jesse Engel, Cinjon Resnick, Adam Roberts, Sander Dieleman, Douglas Eck, Karen Simonyan, Mohammad Norouzi2017-04-05 (AI/music; similar)

“Prediction and Control With Temporal Segment Models”, Mishra Et Al 2017

“Prediction and Control with Temporal Segment Models”⁠, Nikhil Mishra, Pieter Abbeel, Igor Mordatch2017-03-12 (CNN⁠, model-based RL; similar)

“Discovering Objects and Their Relations from Entangled Scene Representations”, Raposo Et Al 2017

“Discovering objects and their relations from entangled scene representations”⁠, David Raposo, Adam Santoro, David Barrett, Razvan Pascanu, Timothy Lillicrap, Peter Battaglia2017-02-16 (meta-learning; similar)

“Categorical Reparameterization With Gumbel-Softmax”, Jang Et Al 2016

“Categorical Reparameterization with Gumbel-Softmax”⁠, Eric Jang, Shixiang Gu, Ben Poole2016-11-03 (DALL·E⁠, statistics/probability; similar)

“The Concrete Distribution: A Continuous Relaxation of Discrete Random Variables”, Maddison Et Al 2016

“The Concrete Distribution: A Continuous Relaxation of Discrete Random Variables”⁠, Chris J. Maddison, Andriy Mnih, Yee Whye Teh2016-11-02 (DALL·E⁠, statistics/probability; similar)

“Improving Sampling from Generative Autoencoders With Markov Chains”, Creswell Et Al 2016

“Improving Sampling from Generative Autoencoders with Markov Chains”⁠, Antonia Creswell, Kai Arulkumaran, Anil Anthony Bharath2016-10-28 (diffusion model; similar)

“Neural Photo Editing With Introspective Adversarial Networks”, Brock Et Al 2016

“Neural Photo Editing with Introspective Adversarial Networks”⁠, Andrew Brock, Theodore Lim, J. M. Ritchie, Nick Weston2016-09-22 (CNN⁠, GAN; backlinks; similar)

“Early Visual Concept Learning With Unsupervised Deep Learning”, Higgins Et Al 2016

“Early Visual Concept Learning with Unsupervised Deep Learning”⁠, Irina Higgins, Loic Matthey, Xavier Glorot, Arka Pal, Benigno Uria, Charles Blundell, Shakir Mohamed2016-06-17 (similar)

“Improving Variational Inference With Inverse Autoregressive Flow”, Kingma Et Al 2016

“Improving Variational Inference with Inverse Autoregressive Flow”⁠, Diederik P. Kingma, Tim Salimans, Rafal Jozefowicz, Xi Chen, Ilya Sutskever, Max Welling2016-06-15 (similar)

“How Far Can We Go without Convolution: Improving Fully-connected Networks”, Lin Et Al 2015

“How far can we go without convolution: Improving fully-connected networks”⁠, Zhouhan Lin, Rol, Memisevic, Kishore Konda2015-11-09 (MLP NN; backlinks; similar)

“Semi-supervised Sequence Learning”, Dai & Le2015

“Semi-supervised Sequence Learning”⁠, Andrew M. Dai, Quoc V. Le2015-11-04 (RNN⁠, NN sampling; backlinks; similar)

“MADE: Masked Autoencoder for Distribution Estimation”, Germain Et Al 2015

“MADE: Masked Autoencoder for Distribution Estimation”⁠, Mathieu Germain, Karol Gregor, Iain Murray, Hugo Larochelle2015-02-12

“Analyzing Noise in Autoencoders and Deep Networks”, Poole Et Al 2014

“Analyzing noise in autoencoders and deep networks”⁠, Ben Poole, Jascha Sohl-Dickstein, Surya Ganguli2014-06-06 (similar)

“Auto-Encoding Variational Bayes”, Kingma & Welling2013

“Auto-Encoding Variational Bayes”⁠, Diederik P. Kingma, Max Welling2013-12-20 (Bayes; similar)

“A Connection Between Score Matching and Denoising Autoencoders”, Vincent2011

“A Connection Between Score Matching and Denoising Autoencoders”⁠, Pascal Vincent2011-07-01 (diffusion model; similar; bibliography)

“Transformers As Variational Autoencoders”

“Randomly Traversing the Manifold of Faces (2): Dataset: Labeled Faces in the Wild (LFW); Model: Variational Auto-Encoder (VAE) /​ Deep Latent Gaussian Model (DLGM).”

“Randomly traversing the manifold of faces (2): Dataset: Labeled Faces in the Wild (LFW); Model: Variational Auto-Encoder (VAE) / Deep Latent Gaussian Model (DLGM).”

Wikipedia

1. Autoencoder

2. Autoencoder § Sparse autoencoder (SAE)

3. Kullback-Leibler divergence

4. Variational Bayesian methods

5. Variational autoencoder