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In generative modeling, numerous successful approaches leverage a low-dimensional latent space, e.g., Stable Diffusion models the latent space induced by an encoder and generates images through a paired decoder.
Neural networks and principal component analysis: Learning from examples without local minima
Pierre Baldi and Kurt Hornik · 1989
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The nature of statistical learning theory
Vladimir Vapnik · 1999
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Stochastic neighbor embedding
Geoffrey Hinton and Sam T Roweis · 2002
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Improved baselines with momentum contrastive learning
Xinlei Chen, Haoqi Fan, Ross Girshick, and Kaiming He · 2003
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Visualizing data using t-sne
Laurens Van der Maaten and Geoffrey Hinton · 2008
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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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Learning multiple layers of features from tiny images
Alex Krizhevsky · 2009
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Gromov–wasserstein distances and the metric approach to object matching
Facundo Mémoli · 2011
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Auto-encoding variational bayes
Diederik P Kingma and Max Welling · 2013
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One millisecond face alignment with an ensemble of regression trees
Vahid Kazemi and Josephine Sullivan · 2014
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Dropout: a simple way to prevent neural networks from overfitting
Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov · 2014
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Deep learning face attributes in the wild
Ziwei Liu, Ping Luo, Xiaogang Wang, and Xiaoou Tang · 2015
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Alireza Makhzani, Jonathon Shlens, Navdeep Jaitly, Ian Goodfellow, and Brendan Frey · 2015
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Unsupervised representation learning with deep convolutional generative adversarial networks
Alec Radford, Luke Metz, and Soumith Chintala · 2015
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Autoencoding beyond pixels using a learned similarity metric
Anders Boesen Lindbo Larsen, Søren Kaae Sønderby, Hugo Larochelle, and Ole Winther · 2016
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f-gan: Training generative neural samplers using variational divergence minimization
Sebastian Nowozin, Botond Cseke, and Ryota Tomioka · 2016
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Wavenet: A generative model for raw audio
Aaron van den Oord, Sander Dieleman, Heiga Zen, Karen Simonyan, Oriol Vinyals, Alex Graves, Nal Kalchbrenner, Andrew Senior, and Koray Kavukcuoglu · 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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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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Mmd gan: Towards deeper understanding of moment matching network
Chun-Liang Li, Wei-Cheng Chang, Yu Cheng, Yiming Yang, and Barnabás Póczos · 2017
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Decoupled weight decay regularization
Ilya Loshchilov and Frank Hutter · 2017
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Adversarial generation of natural language
Sandeep Subramanian, Sai Rajeswar, Francis Dutil, Chris Pal, and Aaron C. Courville · 2017
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Ilya Tolstikhin, Olivier Bousquet, Sylvain Gelly, and Bernhard Schoelkopf · 2017
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Neural discrete representation learning
Aaron Van Den Oord, Oriol Vinyals, et al · 2017
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Seqgan: Sequence generative adversarial nets with policy gradient
Lantao Yu, Weinan Zhang, Jun Wang, and Yong Yu · 2017
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Fixing a broken elbo
Alexander Alemi, Ben Poole, Ian Fischer, Joshua Dillon, Rif A Saurous, and Kevin Murphy · 2018
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Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2018
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Vae with a vampprior
Jakub Tomczak and Max Welling · 2018
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Bourgan: Generative networks with metric embeddings
Chang Xiao, Peilin Zhong, and Changxi Zheng · 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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Ae-ot: A new generative model based on extended semi-discrete optimal transport
Dongsheng An, Yang Guo, Na Lei, Zhongxuan Luo, Shing-Tung Yau, and Xianfeng Gu · 2019
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Intrinsic dimension of data representations in deep neural networks
Alessio Ansuini, Alessandro Laio, Jakob H Macke, and Davide Zoccolan · 2019
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Diagnosing and enhancing vae models
Bin Dai and David Wipf · 2019
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Lagging inference networks and posterior collapse in variational autoencoders
Junxian He, Daniel Spokoyny, Graham Neubig, and Taylor Berg-Kirkpatrick · 2019
Exploring simple siamese representation learning
Xinlei Chen and Kaiming He · 2021
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Taming transformers for high-resolution image synthesis
Patrick Esser, Robin Rombach, and Bjorn Ommer · 2021
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Uncertainty principles of encoding gans
Ruili Feng, Zhouchen Lin, Jiapeng Zhu, Deli Zhao, Jingren Zhou, and Zheng-Jun Zha · 2021
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Masked autoencoders are scalable vision learners
Kaiming He, Xinlei Chen, Saining Xie, Yanghao Li, Piotr Dollár, and Ross Girshick · 2021
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Regularization matters: A nonparametric perspective on overparametrized neural network
Tianyang Hu, Wenjia Wang, Cong Lin, and Guang Cheng · 2021
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The power of contrast for feature learning: A theoretical analysis
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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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Challenging common assumptions in the unsupervised learning of disentangled representations
Francesco Locatello, Stefan Bauer, Mario Lucic, Gunnar Raetsch, Sylvain Gelly, Bernhard Schölkopf, and Olivier Bachem · 2019
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Don’t blame the elbo! a linear vae perspective on posterior collapse
James Lucas, George Tucker, Roger B Grosse, and Mohammad Norouzi · 2019
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Language models are unsupervised multitask learners
Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, Ilya Sutskever, et al · 2019
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Preventing posterior collapse with delta-vaes
Ali Razavi, Aäron van den Oord, Ben Poole, and Oriol Vinyals · 2019
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Discriminator optimal transport
Akinori Tanaka · 2019
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Nonparametric density estimation & convergence rates for gans under besov ipm losses
Ananya Uppal, Shashank Singh, and Barnabás Póczos · 2019
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Wenlong Ji, Zhun Deng, Ryumei Nakada, James Zou, and Linjun Zhang · 2021
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Understanding dimensional collapse in contrastive self-supervised learning
Li Jing, Pascal Vincent, Yann LeCun, and Yuandong Tian · 2021
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Alias-free generative adversarial networks
Tero Karras, Miika Aittala, Samuli Laine, Erik Härkönen, Janne Hellsten, Jaakko Lehtinen, and Timo Aila · 2021
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How well generative adversarial networks learn distributions
Tengyuan Liang · 2021
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The intrinsic dimension of images and its impact on learning
Phillip Pope, Chen Zhu, Ahmed Abdelkader, Micah Goldblum, and Tom Goldstein · 2021
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Zero-shot text-to-image generation
Aditya Ramesh, Mikhail Pavlov, Gabriel Goh, Scott Gray, Chelsea Voss, Alec Radford, Mark Chen, and Ilya Sutskever · 2021
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Gromov-wasserstein distances between gaussian distributions
Antoine Salmona, Julie Delon, and Agnès Desolneux · 2021
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Score-based generative modeling in latent space
Arash Vahdat, Karsten Kreis, and Jan Kautz · 2021
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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 · 2021
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Randall Balestriero and Yann LeCun · 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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Jonathan Ho, Tim Salimans, Alexey Gritsenko, William Chan, Mohammad Norouzi, and David J Fleet · 2022
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Equivariant diffusion for molecule generation in 3d
Emiel Hoogeboom, Vıctor Garcia Satorras, Clément Vignac, and Max Welling · 2022
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Improving generative adversarial networks via adversarial learning in latent space
Yang Li, Yichuan Mo, Liangliang Shi, and Junchi Yan · 2022
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Scalable diffusion models with transformers
William Peebles and Saining Xie · 2022
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Hierarchical text-conditional image generation with clip latents
Aditya Ramesh, Prafulla Dhariwal, Alex Nichol, Casey Chu, and Mark Chen · 2022
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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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Photorealistic text-to-image diffusion models with deep language understanding
Chitwan Saharia, William Chan, Saurabh Saxena, Lala Li, Jay Whang, Emily Denton, Seyed Kamyar Seyed Ghasemipour, Burcu Karagol Ayan, S Sara Mahdavi, Rapha Gontijo Lopes, et al · 2022
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On the capacity of deep generative networks for approximating distributions
Yunfei Yang, Zhen Li, and Yang Wang · 2022
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Scaling autoregressive models for content-rich text-to-image generation
Jiahui Yu, Yuanzhong Xu, Jing Yu Koh, Thang Luong, Gunjan Baid, Zirui Wang, Vijay Vasudevan, Alexander Ku, Yinfei Yang, Burcu Karagol Ayan, et al · 2022
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Scaling vision transformers
Xiaohua Zhai, Alexander Kolesnikov, Neil Houlsby, and Lucas Beyer · 2022
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Muse: Text-to-image generation via masked generative transformers
Huiwen Chang, Han Zhang, Jarred Barber, AJ Maschinot, Jose Lezama, Lu Jiang, Ming-Hsuan Yang, Kevin Murphy, William T Freeman, Michael Rubinstein, et al · 2023
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Your contrastive learning is secretly doing stochastic neighbor embedding
Tianyang Hu, Zhili Liu, Fengwei Zhou, Wenjia Wang, and Weiran Huang · 2023
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Exact count of boundary pieces of relu classifiers: Towards the proper complexity measure for classification
Paweł Piwek, Adam Klukowski, and Tianyang Hu · 2023
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