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Vision tokenizers have gained a lot of attraction due to their scalability and compactness; previous works depend on old-school GAN-based hyperparameters, biased comparisons, and a lack of comprehensive analysis of the scaling behaviours.
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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Product quantization for nearest neighbor search
Herve Jegou, Matthijs Douze, and Cordelia Schmid · 2010
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Auto-encoding variational bayes
Diederik P Kingma · 2013
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Very deep convolutional networks for large-scale image recognition
Karen Simonyan and Andrew Zisserman · 2015
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Improved training of wasserstein gans, 2017
Ishaan Gulrajani, Faruk Ahmed, Martin Arjovsky, Vincent Dumoulin, and Aaron Courville · 2017
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beta-vae: Learning basic visual concepts with a constrained variational framework
Irina Higgins, Loic Matthey, Arka Pal, Christopher P Burgess, Xavier Glorot, Matthew M Botvinick, Shakir Mohamed, and Alexander Lerchner · 2017
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Arbitrary style transfer in real-time with adaptive instance normalization
Xun Huang and Serge Belongie · 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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Neural discrete representation learning
Aaron Van Den Oord, Oriol Vinyals, et al · 2017
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f-vaes: Improve vaes with conditional flows
Jianlin Su and Guang Wu · 2018
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The unreasonable effectiveness of deep features as a perceptual metric
Richard Zhang, Phillip Isola, Alexei A Efros, Eli Shechtman, and Oliver Wang · 2018
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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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Bayes-factor-vae: Hierarchical bayesian deep auto-encoder models for factor disentanglement
Minyoung Kim, Yuting Wang, Pritish Sahu, and Vladimir Pavlovic · 2019
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Generating diverse high-fidelity images with vq-vae-2
Ali Razavi, Aaron Van den Oord, and Oriol Vinyals · 2019
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dpvaes: Fixing sample generation for regularized vaes
Riddhish Bhalodia, Iain Lee, and Shireen Elhabian · 2020
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Nvae: A deep hierarchical variational autoencoder
Arash Vahdat and Jan Kautz · 2020
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Boovae: Boosting approach for continual learning of vae
Evgenii Egorov, Anna Kuzina, and Evgeny Burnaev · 2021
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Taming transformers for high-resolution image synthesis
Patrick Esser, Robin Rombach, and Bjorn Ommer · 2021
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Learning transferable visual models from natural language supervision
Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al · 2021
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Soundstream: An end-to-end neural audio codec
Neil Zeghidour, Alejandro Luebs, Ahmed Omran, Jan Skoglund, and Marco Tagliasacchi · 2021
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Hierarchical residual learning based vector quantized variational autoencorder for image reconstruction and generation
Mohammad Adiban, Marco Siniscalchi, Kalin Stefanov, and Giampiero Salvi · 2022
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Maskgit: Masked generative image transformer
Huiwen Chang, Han Zhang, Lu Jiang, Ce Liu, and William T Freeman · 2022
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Masked autoencoders are scalable vision learners
Kaiming He, Xinlei Chen, Saining Xie, Yanghao Li, Piotr Dollár, and Ross Girshick · 2022
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Matrix information theory for self-supervised learning
Yifan Zhang, Zhiquan Tan, Jingqin Yang, Weiran Huang, and Yang Yuan · 2023
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Restructuring vector quantization with the rotation trick, 2024
Christopher Fifty, Ronald G. Junkins, Dennis Duan, Aniketh Iger, Jerry W. Liu, Ehsan Amid, Sebastian Thrun, and Christopher Ré · 2024
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Peng Gao, Le Zhuo, Chris Liu, , Ruoyi Du, Xu Luo, Longtian Qiu, Yuhang Zhang, et al · 2024
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Zigma: A dit-style zigzag mamba diffusion model
Vincent Tao Hu, Stefan Andreas Baumann, Ming Gui, Olga Grebenkova, Pingchuan Ma, Johannes Fischer, and Björn Ommer · 2024
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Videopoet: A large language model for zero-shot video generation
Dan Kondratyuk, Lijun Yu, Xiuye Gu, Jose Lezama, Jonathan Huang, Grant Schindler, Rachel Hornung, Vighnesh Birodkar, Jimmy Yan, Ming-Chang Chiu, Krishna Somandepalli, Hassan Akbari, Yair Alon, Yong Cheng, Joshua V. Dillon, Agrim Gupta, Meera Hahn, Anja Hauth, David Hendon, Alonso Martinez, David Minnen, Mikhail Sirotenko, Kihyuk Sohn, Xuan Yang, Hartwig Adam, Ming-Hsuan Yang, Irfan Essa, Huisheng Wang, David A Ross, Bryan Seybold, and Lu Jiang · 2024
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Doyup Lee, Chiheon Kim, Saehoon Kim, Minsu Cho, and Wook-Shin Han · 2022
Cited alongside, same era.
Optimizing hierarchical image vaes for sample quality
Eric Luhman and Troy Luhman · 2022
Cited alongside, same era.
Beit v2: Masked image modeling with vector-quantized visual tokenizers, 2022
Zhiliang Peng, Li Dong, Hangbo Bao, Qixiang Ye, and Furu Wei · 2022
Cited alongside, same era.
High-resolution image synthesis with latent diffusion models
Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer · 2022
Cited alongside, same era.
Locally hierarchical auto-regressive modeling for image generation
Tackgeun You, Saehoon Kim, Chiheon Kim, Doyup Lee, and Bohyung Han · 2022
Cited alongside, same era.
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 Baldridge, and Yonghui Wu · 2022
Cited alongside, same era.
Mohammad Adiban, Kalin Stefanov, Sabato Marco Siniscalchi, and Giampiero Salvi · 2023
Cited alongside, same era.
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High-fidelity audio compression with improved rvqgan
Rithesh Kumar, Prem Seetharaman, Alejandro Luebs, Ishaan Kumar, and Kundan Kumar · 2024
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Imagefolder: Autoregressive image generation with folded tokens
Xiang Li, Hao Chen, Kai Qiu, Jason Kuen, Jiuxiang Gu, Bhiksha Raj, and Zhe Lin · 2024
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Open-magvit2: An open-source project toward democratizing auto-regressive visual generation
Zhuoyan Luo, Fengyuan Shi, Yixiao Ge, Yujiu Yang, Limin Wang, and Ying Shan · 2024
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Finite scalar quantization: VQ-VAE made simple
Fabian Mentzer, David Minnen, Eirikur Agustsson, and Michael Tschannen · 2024
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DINOv2: Learning robust visual features without supervision
Maxime Oquab, Timothée Darcet, Théo Moutakanni, Huy V. Vo, Marc Szafraniec, Vasil Khalidov, Pierre Fernandez, Daniel HAZIZA, Francisco Massa, Alaaeldin El-Nouby, Mido Assran, Nicolas Ballas, Wojciech Galuba, Russell Howes, Po-Yao Huang, Shang-Wen Li, Ishan Misra, Michael Rabbat, Vasu Sharma, Gabriel Synnaeve, Hu Xu, Herve Jegou, Julien Mairal, Patrick Labatut, Armand Joulin, and Piotr Bojanowski · 2024
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Epanechnikov variational autoencoder
Tian Qin and Wei-Min Huang · 2024
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Litevae: Lightweight and efficient variational autoencoders for latent diffusion models
Seyedmorteza Sadat, Jakob Buhmann, Derek Bradley, Otmar Hilliges, and Romann M Weber · 2024
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Autoregressive model beats diffusion: Llama for scalable image generation
Peize Sun, Yi Jiang, Shoufa Chen, Shilong Zhang, Bingyue Peng, Ping Luo, and Zehuan Yuan · 2024
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Visual autoregressive modeling: Scalable image generation via next-scale prediction
Keyu Tian, Yi Jiang, Zehuan Yuan, Bingyue Peng, and Liwei Wang · 2024
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Maskbit: Embedding-free image generation via bit tokens
Mark Weber, Lijun Yu, Qihang Yu, Xueqing Deng, Xiaohui Shen, Daniel Cremers, and Liang-Chieh Chen · 2024
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Cogvideox: Text-to-video diffusion models with an expert transformer
Zhuoyi Yang, Jiayan Teng, Wendi Zheng, Ming Ding, Shiyu Huang, Jiazheng Xu, Yuanming Yang, Wenyi Hong, Xiaohan Zhang, Guanyu Feng, et al · 2024
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Image and video tokenization with binary spherical quantization
Yue Zhao, Yuanjun Xiong, and Philipp Krähenbühl · 2024
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Wavelet-based image tokenizer for vision transformers
Zhenhai Zhu and Radu Soricut · 2024
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