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Vector Quantized Variational AutoEncoders (VQ-VAEs) are designed to compress a continuous input to a discrete latent space and reconstruct it with minimal distortion.
Vector quantization
Robert Gray · 1984
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Autoencoders, minimum description length and helmholtz free energy
Geoffrey E Hinton and Richard Zemel · 1993
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Elements of information theory
Thomas M Cover · 1999
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A model of inductive bias learning
Jonathan Baxter · 2000
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Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
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Ucf101: A dataset of 101 human actions classes from videos in the wild
K Soomro · 2012
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Estimating or propagating gradients through stochastic neurons for conditional computation
Yoshua Bengio, Nicholas Léonard, and Aaron Courville · 2013
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Auto-encoding variational bayes
Diederik P Kingma and Max Welling · 2013
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Categorical reparameterization with gumbel-softmax
Eric Jang, Shixiang Gu, and Ben Poole · 2016
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Perceptual losses for real-time style transfer and super-resolution
Justin Johnson, Alexandre Alahi, and Li Fei-Fei · 2016
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Improved techniques for training gans
Tim Salimans, Ian Goodfellow, Wojciech Zaremba, Vicki Cheung, Alec Radford, and Xi Chen · 2016
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Self-supervised visual planning with temporal skip connections
Frederik Ebert, Chelsea Finn, Alex X Lee, and Sergey Levine · 2017
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Gans trained by a two time-scale update rule converge to a local nash equilibrium
Martin Heusel, Hubert Ramsauer, Thomas Unterthiner, Bernhard Nessler, and Sepp Hochreiter · 2017
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Image-to-image translation with conditional adversarial networks
Phillip Isola, Jun-Yan Zhu, Tinghui Zhou, and Alexei A Efros · 2017
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Progressive growing of gans for improved quality, stability, and variation
Tero Karras · 2017
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Neural discrete representation learning
Aaron Van Den Oord, Oriol Vinyals, et al · 2017
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Attention is all you need
A Vaswani · 2017
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Gradnorm: Gradient normalization for adaptive loss balancing in deep multitask networks
Zhao Chen, Vijay Badrinarayanan, Chen-Yu Lee, and Andrew Rabinovich · 2018
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Multi-task learning using uncertainty to weigh losses for scene geometry and semantics
Alex Kendall, Yarin Gal, and Roberto Cipolla · 2018
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Towards accurate generative models of video: A new metric & challenges
Thomas Unterthiner, Sjoerd Van Steenkiste, Karol Kurach, Raphael Marinier, Marcin Michalski, and Sylvain Gelly · 2018
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vq-wav2vec: Self-supervised learning of discrete speech representations
Alexei Baevski, Steffen Schneider, and Michael Auli · 2019
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A style-based generator architecture for generative adversarial networks
Tero Karras, Samuli Laine, and Timo Aila · 2019
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High-resolution image synthesis with latent diffusion models
Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer · 2022
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Sq-vae: Variational bayes on discrete representation with self-annealed stochastic quantization
Yuhta Takida, Takashi Shibuya, WeiHsiang Liao, Chieh-Hsin Lai, Junki Ohmura, Toshimitsu Uesaka, Naoki Murata, Shusuke Takahashi, Toshiyuki Kumakura, and Yuki Mitsufuji · 2022
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Peco: Perceptual codebook for bert pre-training of vision transformers
Xiaoyi Dong, Jianmin Bao, Ting Zhang, Dongdong Chen, Weiming Zhang, Lu Yuan, Dong Chen, Fang Wen, Nenghai Yu, and Baining Guo · 2023
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Soft convex quantization: Revisiting vector quantization with convex optimization
Tanmay Gautam, Reid Pryzant, Ziyi Yang, Chenguang Zhu, and Somayeh Sojoudi · 2023
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Towards accurate image coding: Improved autoregressive image generation with dynamic vector quantization
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Jukebox: A generative model for music
Prafulla Dhariwal, Heewoo Jun, Christine Payne, Jong Wook Kim, Alec Radford, and Ilya Sutskever · 2020
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An image is worth 16x16 words: Transformers for image recognition at scale
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Robust training of vector quantized bottleneck models
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Taming transformers for high-resolution image synthesis
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Videogpt: Video generation using vq-vae and transformers
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Vector-quantized image modeling with improved vqgan
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Mengqi Huang, Zhendong Mao, Zhuowei Chen, and Yongdong Zhang · 2023
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Straightening out the straight-through estimator: Overcoming optimization challenges in vector quantized networks
Minyoung Huh, Brian Cheung, Pulkit Agrawal, and Phillip Isola · 2023
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Finite scalar quantization: Vq-vae made simple
Fabian Mentzer, David Minnen, Eirikur Agustsson, and Michael Tschannen · 2023
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Language model beats diffusion–tokenizer is key to visual generation
Lijun Yu, José Lezama, Nitesh B Gundavarapu, Luca Versari, Kihyuk Sohn, David Minnen, Yong Cheng, Agrim Gupta, Xiuye Gu, Alexander G Hauptmann, et al · 2023
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Regularized vector quantization for tokenized image synthesis
Jiahui Zhang, Fangneng Zhan, Christian Theobalt, and Shijian Lu · 2023
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Online clustered codebook
Chuanxia Zheng and Andrea Vedaldi · 2023
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Designing a better asymmetric vqgan for stablediffusion
Zixin Zhu, Xuelu Feng, Dongdong Chen, Jianmin Bao, Le Wang, Yinpeng Chen, Lu Yuan, and Gang Hua · 2023
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Video generation models as world simulators
Tim Brooks, Bill Peebles, Connor Holmes, Will DePue, Yufei Guo, Li Jing, David Schnurr, Joe Taylor, Troy Luhman, Eric Luhman, Clarence Ng, Ricky Wang, and Aditya Ramesh · 2024
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Balance of number of embedding and their dimensions in vector quantization
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How to compute hessian-vector products?
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Hypervq: Mlr-based vector quantization in hyperbolic space
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Image and video tokenization with binary spherical quantization
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