“‘Masked Autoencoder’ Tag”,2021-11-11 (LM tokenization, Transformer, AI scaling, video analysis)⁠:

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”, Bai et al 2024
  • “MaskBit: Embedding-Free Image Generation via Bit Tokens”, Weber et al 2024
  • “Stretching Each Dollar: Diffusion Training from Scratch on a Micro-Budget”, Sehwag et al 2024
  • “Diffusion Forcing: Next-Token Prediction Meets Full-Sequence Diffusion”, Chen et al 2024
  • “MAR: Autoregressive Image Generation without Vector Quantization”, Li et al 2024
  • “SemantiCodec: An Ultra Low Bitrate Semantic Audio Codec for General Sound”, Liu et al 2024
  • “Σ-GPTs: A New Approach to Autoregressive Models”, Pannatier et al 2024
  • “Rethinking Patch Dependence for Masked Autoencoders”, Fu et al 2024
  • “Rich Human Feedback for Text-To-Image Generation”, Liang et al 2023
  • “Self-Conditioned Image Generation via Generating Representations”, Li et al 2023
  • “Rethinking FID: Towards a Better Evaluation Metric for Image Generation”, Jayasumana et al 2023
  • “Learning Unsupervised World Models for Autonomous Driving via Discrete Diffusion”, Zhang et al 2023
  • “Limited Data, Unlimited Potential: A Study on ViTs Augmented by Masked Autoencoders (SSAT)”, Das et al 2023
  • “Vision Transformers Need Registers”, Darcet et al 2023
  • “Diffusion Models Beat GANs on Image Classification”, Mukhopadhyay et al 2023
  • “Test-Time Training on Video Streams”, Wang et al 2023
  • “Rosetta Neurons: Mining the Common Units in a Model Zoo”, Dravid et al 2023
  • “Exposing Flaws of Generative Model Evaluation Metrics and Their Unfair Treatment of Diffusion Models”, Stein et al 2023
  • “Generalizable Synthetic Image Detection via Language-Guided Contrastive Learning”, Wu et al 2023
  • “SoundStorm: Efficient Parallel Audio Generation”, Borsos et al 2023
  • “A Cookbook of Self-Supervised Learning”, Balestriero et al 2023
  • “CLaMP: Contrastive Language-Music Pre-Training for Cross-Modal Symbolic Music Information Retrieval”, Wu et al 2023
  • “Masked Diffusion Transformer Is a Strong Image Synthesizer”, Gao et al 2023
  • “PixMIM: Rethinking Pixel Reconstruction in Masked Image Modeling”, Liu et al 2023
  • “John Carmack’s ‘Different Path’ to Artificial General Intelligence”, Carmack2023
  • “JEPA: Self-Supervised Learning from Images With a Joint-Embedding Predictive Architecture”, Assran et al 2023
  • “MUG: Vision Learners Meet Web Image-Text Pairs”, Zhao et al 2023
  • “TinyMIM: An Empirical Study of Distilling MIM Pre-Trained Models”, Ren et al 2023
  • “Muse: Text-To-Image Generation via Masked Generative Transformers”, Chang et al 2023
  • “MAGVIT: Masked Generative Video Transformer”, Yu et al 2022
  • “Scaling Language-Image Pre-Training via Masking”, Li et al 2022
  • “MaskDistill: A Unified View of Masked Image Modeling”, Anonymous2022
  • “MAGE: MAsked Generative Encoder to Unify Representation Learning and Image Synthesis”, Li et al 2022
  • “Paella: Fast Text-Conditional Discrete Denoising on Vector-Quantized Latent Spaces”, Rampas et al 2022
  • “EVA: Exploring the Limits of Masked Visual Representation Learning at Scale”, Fang et al 2022
  • “Exploring Long-Sequence Masked Autoencoders”, Hu et al 2022
  • “TVLT: Textless Vision-Language Transformer”, Tang et al 2022
  • “Test-Time Training With Masked Autoencoders”, Gandelsman et al 2022
  • “PatchDropout: Economizing Vision Transformers Using Patch Dropout”, Liu et al 2022
  • “CMAE: Contrastive Masked Autoencoders Are Stronger Vision Learners”, Huang et al 2022
  • “PIXEL: Language Modeling With Pixels”, Rust et al 2022
  • “Masked Autoencoders That Listen”, Po-Yao et al 2022
  • “OmniMAE: Single Model Masked Pretraining on Images and Videos”, Girdhar et al 2022
  • “M3AE: Multimodal Masked Autoencoders Learn Transferable Representations”, Geng et al 2022
  • “Masked Autoencoders As Spatiotemporal Learners”, Feichtenhofer et al 2022
  • “CogView2: Faster and Better Text-To-Image Generation via Hierarchical Transformers”, Ding et al 2022
  • “Should You Mask 15% in Masked Language Modeling?”, Wettig et al 2022
  • “MaskGIT: Masked Generative Image Transformer”, Chang et al 2022
  • “SimMIM: A Simple Framework for Masked Image Modeling”, Xie et al 2021
  • “MAE: Masked Autoencoders Are Scalable Vision Learners”, He et al 2021
  • “Hide-And-Seek: A Data Augmentation Technique for Weakly-Supervised Localization and Beyond”, Singh et al 2018
  • Sort By Magic
    • semantic-audio
    • image-generation
    • masked-image-modeling
    • masked-autoencoders
    • test-time-training
• Miscellaneous
• Bibliography