“‘Masked Autoencoder’ Tag”,2021-11-11 ():
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Bibliography for tag
ai/nn/vae/mae, most recent first: 51 annotations & 6 links (parent).
- See Also
- Links
- “Meissonic: Revitalizing Masked Generative Transformers for Efficient High-Resolution Text-To-Image Synthesis”, et al 2024
- “MaskBit: Embedding-Free Image Generation via Bit Tokens”, et al 2024
- “Stretching Each Dollar: Diffusion Training from Scratch on a Micro-Budget”, et al 2024
- “Diffusion Forcing: Next-Token Prediction Meets Full-Sequence Diffusion”, et al 2024
- “MAR: Autoregressive Image Generation without Vector Quantization”, et al 2024
- “SemantiCodec: An Ultra Low Bitrate Semantic Audio Codec for General Sound”, et al 2024
- “Σ-GPTs: A New Approach to Autoregressive Models”, et al 2024
- “Rethinking Patch Dependence for Masked Autoencoders”, et al 2024
- “Rich Human Feedback for Text-To-Image Generation”, et al 2023
- “Self-Conditioned Image Generation via Generating Representations”, et al 2023
- “Rethinking FID: Towards a Better Evaluation Metric for Image Generation”, et al 2023
- “Learning Unsupervised World Models for Autonomous Driving via Discrete Diffusion”, et al 2023
- “Limited Data, Unlimited Potential: A Study on ViTs Augmented by Masked Autoencoders (SSAT)”, et al 2023
- “Vision Transformers Need Registers”, et al 2023
- “Diffusion Models Beat GANs on Image Classification”, et al 2023
- “Test-Time Training on Video Streams”, et al 2023
- “Rosetta Neurons: Mining the Common Units in a Model Zoo”, et al 2023
- “Exposing Flaws of Generative Model Evaluation Metrics and Their Unfair Treatment of Diffusion Models”, et al 2023
- “Generalizable Synthetic Image Detection via Language-Guided Contrastive Learning”, et al 2023
- “SoundStorm: Efficient Parallel Audio Generation”, et al 2023
- “A Cookbook of Self-Supervised Learning”, et al 2023
- “CLaMP: Contrastive Language-Music Pre-Training for Cross-Modal Symbolic Music Information Retrieval”, et al 2023
- “Masked Diffusion Transformer Is a Strong Image Synthesizer”, et al 2023
- “PixMIM: Rethinking Pixel Reconstruction in Masked Image Modeling”, et al 2023
- “John Carmack’s ‘Different Path’ to Artificial General Intelligence”, 2023
- “JEPA: Self-Supervised Learning from Images With a Joint-Embedding Predictive Architecture”, et al 2023
- “MUG: Vision Learners Meet Web Image-Text Pairs”, et al 2023
- “TinyMIM: An Empirical Study of Distilling MIM Pre-Trained Models”, et al 2023
- “Muse: Text-To-Image Generation via Masked Generative Transformers”, et al 2023
- “MAGVIT: Masked Generative Video Transformer”, et al 2022
- “Scaling Language-Image Pre-Training via Masking”, et al 2022
- “MaskDistill: A Unified View of Masked Image Modeling”, 2022
- “MAGE: MAsked Generative Encoder to Unify Representation Learning and Image Synthesis”, et al 2022
- “Paella: Fast Text-Conditional Discrete Denoising on Vector-Quantized Latent Spaces”, et al 2022
- “EVA: Exploring the Limits of Masked Visual Representation Learning at Scale”, et al 2022
- “Exploring Long-Sequence Masked Autoencoders”, et al 2022
- “TVLT: Textless Vision-Language Transformer”, et al 2022
- “Test-Time Training With Masked Autoencoders”, et al 2022
- “PatchDropout: Economizing Vision Transformers Using Patch Dropout”, et al 2022
- “CMAE: Contrastive Masked Autoencoders Are Stronger Vision Learners”, et al 2022
- “PIXEL: Language Modeling With Pixels”, et al 2022
- “Masked Autoencoders That Listen”, Po- et al 2022
- “OmniMAE: Single Model Masked Pretraining on Images and Videos”, et al 2022
- “M3AE: Multimodal Masked Autoencoders Learn Transferable Representations”, et al 2022
- “Masked Autoencoders As Spatiotemporal Learners”, et al 2022
- “CogView2: Faster and Better Text-To-Image Generation via Hierarchical Transformers”, et al 2022
- “Should You Mask 15% in Masked Language Modeling?”, et al 2022
- “MaskGIT: Masked Generative Image Transformer”, et al 2022
- “SimMIM: A Simple Framework for Masked Image Modeling”, et al 2021
- “MAE: Masked Autoencoders Are Scalable Vision Learners”, et al 2021
- “Hide-And-Seek: A Data Augmentation Technique for Weakly-Supervised Localization and Beyond”, et al 2018
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