Conference papers on deep generative models.
- Your Classifier is Secretly an Energy based Model and You Should Treat it Like One, Will Grathwohl, Kuan-Chieh Wang, Joern-Henrik Jacobsen, David Duvenaud, Mohammad Norouzi, Kevin Swersky
- High Fidelity Speech Synthesis with Adversarial Networks, Mikołaj Bińkowski, Jeff Donahue, Sander Dieleman, Aidan Clark, Erich Elsen, Norman Casagrande, Luis C. Cobo, Karen Simonyan
- Stable Rank Normalization for Improved Generalization in Neural Networks and GANs, Amartya Sanyal, Philip H. Torr, Puneet K. Dokania
- Scaling Autoregressive Video Models, Dirk Weissenborn, Oscar Täckström, Jakob Uszkoreit
- AE-OT: A New Generative Model based on Extended Semi-Discrete Optimal Transport, Dongsheng An, Yang Guo, Na Lei, Zhongxuan Luo, Shing-Tung Yau, Xianfeng Gu
- Input Complexity and Out-of-distribution Detection with Likelihood-based Generative Models, Joan Serrà, David Álvarez, Vicenç Gómez, Olga Slizovskaia, José F. Núñez, Jordi Luque
- VideoFlow: A Conditional Flow-Based Model for Stochastic Video Generation, Manoj Kumar, Mohammad Babaeizadeh, Dumitru Erhan, Chelsea Finn, Sergey Levine, Laurent Dinh, Durk Kingma
- Difference-Seeking Generative Adversarial Network--Unseen Sample Generation, Yi Lin Sung, Sung-Hsien Hsieh, Soo-Chang Pei, Chun-Shien Lu
- From Variational to Deterministic Autoencoders, Partha Ghosh, Mehdi S. M. Sajjadi, Antonio Vergari, Michael Black, Bernhard Scholkopf
- Generative Ratio Matching Networks, Akash Srivastava, Kai Xu, Michael U. Gutmann, Charles Sutton
- On the "Steerability" of Generative Adversarial Networks, Ali Jahanian*, Lucy Chai*, Phillip Isola
- Semi-Supervised Generative Modeling for Controllable Speech Synthesis, Raza Habib, Soroosh Mariooryad, Matt Shannon, Eric Battenberg, RJ Skerry-Ryan, Daisy Stanton, David Kao, Tom Bagby
- Towards Better Understanding of Adaptive Gradient Algorithms in Generative Adversarial Nets, Mingrui Liu, Youssef Mroueh, Jerret Ross, Wei Zhang, Xiaodong Cui, Payel Das, Tianbao Yang
- Variational Hetero-Encoder Randomized GANs for Joint Image-Text Modeling, Hao Zhang, Bo Chen, Long Tian, Zhengjue Wang, Mingyuan Zhou
- On the Need for Topology-Aware Generative Models for Manifold-Based Defenses, Uyeong Jang, Susmit Jha, Somesh Jha
- A Closer Look at the Optimization Landscapes of Generative Adversarial Networks, Hugo Berard, Gauthier Gidel, Amjad Almahairi, Pascal Vincent, Simon Lacoste-Julien
- Generative Models for Effective ML on Private, Decentralized Datasets, Sean Augenstein, H. Brendan McMahan, Daniel Ramage, Swaroop Ramaswamy, Peter Kairouz, Mingqing Chen, Rajiv Mathews, Blaise Aguera y Arcas
- Smoothness and Stability in GANs, Casey Chu, Kentaro Minami, Kenji Fukumizu
- Kernel of CycleGAN as a Principal Homogeneous Space, Nikita Moriakov, Jonas Adler, Jonas Teuwen
- U-GAT-IT: Unsupervised Generative Attentional Networks with Adaptive Layer-Instance Normalization for Image-to-Image Translation, Junho Kim, Minjae Kim, Hyeonwoo Kang, Kwang Hee Lee
- Understanding the Limitations of Conditional Generative Models, Ethan Fetaya, Joern-Henrik Jacobsen, Will Grathwohl, Richard Zemel
- Mixed-curvature Variational Autoencoders, Ondrej Skopek, Octavian-Eugen Ganea, Gary Bécigneul
- Adversarial Lipschitz Regularization, Dávid Terjék
- Consistency Regularization for Generative Adversarial Networks, Han Zhang, Zizhao Zhang, Augustus Odena, Honglak Lee
- The Shape of Data: Intrinsic Distance for Data Distributions, Anton Tsitsulin, Marina Munkhoeva, Davide Mottin, Panagiotis Karras, Alex Bronstein, Ivan Oseledets, Emmanuel Mueller
- Do GANs always have Nash equilibria?, Farzan Farnia, Asuman Ozdaglar
- AutoGAN-Distiller: Searching to Compress Generative Adversarial Networks, Yonggan Fu, Wuyang Chen, Haotao Wang, Haoran Li, Yingyan Lin, Zhangyang Wang
- SimGANs: Simulator-Based Generative Adversarial Networks for ECG Synthesis to Improve Deep ECG Classification, omer Golany, Kira Radinsky, Daniel Freedman
- SGD Learns One-Layer Networks in WGANs, Qi Lei, Jason Lee, Alex Dimakis, Constantinos Daskalakis
- InfoGAN-CR and ModelCentrality: Self-supervised Model Training and Selection for Disentangling GANs, Zinan Lin, Kiran Thekumparampil, Giulia Fanti, Sewoong Oh
- Semi-Supervised StyleGAN for Disentanglement Learning, Weili Nie, Tero Karras, Animesh Garg, Shoubhik Debnath, Anjul Patney, Ankit Patel, Animashree Anandkumar
- Implicit competitive regularization in GANs, Florian Schaefer, Hongkai Zheng, Animashree Anandkumar
- Small-GAN: Speeding up GAN Training using Core-Sets, Samarth Sinha, Han Zhang, Anirudh Goyal, Yoshua Bengio, Hugo Larochelle, Augustus Odena
- Bridging the Gap Between f-GANs and Wasserstein GANs, Jiaming Song, Stefano Ermon
- Learning disconnected manifolds: a no GAN’s land, Ugo Tanielian, Thibaut Issenhuth, Elvis Dohmatob, Jeremie Mary
- Unsupervised Discovery of Interpretable Directions in the GAN Latent Space, Andrey Voynov, Artem Babenko
- Understanding and Stabilizing GANs’ Training Dynamics Using Control Theory, Kun Xu, Chongxuan Li, Jun Zhu, Bo Zhang
- On Leveraging Pretrained GANs for Generation with Limited Data, Miaoyun Zhao, Yulai Cong, Lawrence Carin
- Feature Quantization Improves GAN Training, Yang Zhao, Chunyuan Li, Ping Yu, Jianfeng Gao, Changyou Chen
- Invertible generative models for inverse problems: mitigating representation error and dataset bias, Muhammad Asim, Max Daniels, Oscar Leong, Ali Ahmed, Paul Hand
- VFlow: More Expressive Generative Flows with Variational Data Augmentation, Jianfei Chen, Cheng Lu, Biqi Chenli, Jun Zhu, Tian Tian
- Generative Pretraining From Pixels, Mark Chen, Alec Radford, Rewon Child, Jeffrey Wu, Heewoo Jun, David Luan, Ilya Sutskever
- Fair Generative Modeling via Weak Supervision, Kristy Choi, Aditya Grover, Trisha Singh, Rui Shu, Stefano Ermon
- Evaluating Lossy Compression Rates of Deep Generative Models, Sicong Huang, Alireza Makhzani, Yanshuai Cao, Roger Grosse
- Source Separation with Deep Generative Priors, Vivek Jayaram, John Thickstun
- Distribution Augmentation for Generative Modeling, Heewoo Jun, Rewon Child, Mark Chen, John Schulman, Aditya Ramesh, Alec Radford, Ilya Sutskever
- On the Power of Compressed Sensing with Generative Models, Akshay Kamath, Eric Price, Sushrut Karmalkar
- Equivariant Flows: Exact Likelihood Generative Learning for Symmetric Densities, Jonas Köhler, Leon Klein, Frank Noe
- Reliable Fidelity and Diversity Metrics for Generative Models, Muhammad Ferjad Naeem, Seong Joon Oh, Youngjung Uh, Yunjey Choi, Jaejun Yoo
- PolyGen: An Autoregressive Generative Model of 3D Meshes, Charlie Nash, Yaroslav Ganin, S. M. Ali Eslami, Peter Battaglia
- Implicit Generative Modeling for Efficient Exploration, Neale Ratzlaff, Qinxun Bai, Li Fuxin, Wei Xu
- Generative Teaching Networks: Accelerating Neural Architecture Search by Learning to Generate Synthetic Training Data, Felipe Petroski Such, Aditya Rawal, Joel Lehman, Kenneth Stanley, Jeffrey Clune
- Perceptual Generative Autoencoders, Zijun Zhang, Ruixiang Zhang, Zongpeng Li, Yoshua Bengio, Liam Paull
- Latent Bernoulli Autoencoder, Jiri Fajtl, Vasileios Argyriou, Dorothy Monekosso, Paolo Remagnino
- Variational Autoencoders with Riemannian Brownian Motion Priors, Dimitrios Kalatzis, David Eklund, Georgios Arvanitidis, Soren Hauberg
- Topological Autoencoders, Michael Moor, Max Horn, Bastian Rieck, Karsten Borgwardt
- Eliminating the Invariance on the Loss Landscape of Linear Autoencoders, Reza Oftadeh, Jiayi Shen, Zhangyang Wang, Dylan Shell
- ControlVAE: Controllable Variational Autoencoder, Huajie Shao, Shuochao Yao, Dachun Sun, Aston Zhang, Shengzhong Liu, Dongxin Liu, Jun Wang, Tarek Abdelzaher
- Learning Autoencoders with Relational Regularization, Hongteng Xu, Dixin Luo, Ricardo Henao, Svati Shah, Lawrence Carin
- Teaching a GAN What Not to Learn, Siddarth Asokan, Chandra Seelamantula
- Improving GAN Training with Probability Ratio Clipping and Sample Reweighting, Yue Wu, Pan Zhou, Andrew G. Wilson, Eric Xing, Zhiting Hu
- GramGAN: Deep 3D Texture Synthesis From 2D Exemplars, Tiziano Portenier, Siavash Arjomand Bigdeli, Orcun Goksel
- Differentiable Augmentation for Data-Efficient GAN Training, Shengyu Zhao, Zhijian Liu, Ji Lin, Jun-Yan Zhu, Song Han
- COT-GAN: Generating Sequential Data via Causal Optimal Transport, Tianlin Xu, Li Kevin Wenliang, Michael Munn, Beatrice Acciaio
- GANSpace: Discovering Interpretable GAN Controls, Erik Härkönen, Aaron Hertzmann, Jaakko Lehtinen, Sylvain Paris
- Towards a Better Global Loss Landscape of GANs, Ruoyu Sun, Tiantian Fang, Alexander Schwing
- Your GAN is Secretly an Energy-based Model and You Should Use Discriminator Driven Latent Sampling, Tong Che, Ruixiang ZHANG, Jascha Sohl-Dickstein, Hugo Larochelle, Liam Paull, Yuan Cao, Yoshua Bengio
- GS-WGAN: A Gradient-Sanitized Approach for Learning Differentially Private Generators, Dingfan Chen, Tribhuvanesh Orekondy, Mario Fritz
- Reconstructing Perceptive Images from Brain Activity by Shape-Semantic GAN, Tao Fang, Yu Qi, Gang Pan
- Instance Selection for GANs, Terrance DeVries, Michal Drozdzal, Graham W. Taylor
- Top-k Training of GANs: Improving GAN Performance by Throwing Away Bad Samples, Samarth Sinha, Zhengli Zhao, Anirudh Goyal ALIAS PARTH GOYAL, Colin A. Raffel, Augustus Odena
- GAN Memory with No Forgetting, Yulai Cong, Miaoyun Zhao, Jianqiao Li, Sijia Wang, Lawrence Carin
- Hierarchical Patch VAE-GAN: Generating Diverse Videos from a Single Sample, Shir Gur, Sagie Benaim, Lior Wolf
- HiFi-GAN: Generative Adversarial Networks for Efficient and High Fidelity Speech Synthesis, Jungil Kong, Jaehyeon Kim, Jaekyoung Bae
- Elastic-InfoGAN: Unsupervised Disentangled Representation Learning in Class-Imbalanced Data, Utkarsh Ojha, Krishna Kumar Singh, Cho-Jui Hsieh, Yong Jae Lee
- ColdGANs: Taming Language GANs with Cautious Sampling Strategies, Thomas Scialom, Paul-Alexis Dray, Sylvain Lamprier, Benjamin Piwowarski, Jacopo Staiano
- CircleGAN: Generative Adversarial Learning across Spherical Circles, Woohyeon Shim, Minsu Cho
- ContraGAN: Contrastive Learning for Conditional Image Generation, Minguk Kang, Jaesik Park
- Sinkhorn Natural Gradient for Generative Models, Zebang Shen, Zhenfu Wang, Alejandro Ribeiro, Hamed Hassani
- A Loss Function for Generative Neural Networks Based on Watson’s Perceptual Model, Steffen Czolbe, Oswin Krause, Ingemar Cox, Christian Igel
- Asymptotic Guarantees for Generative Modeling Based on the Smooth Wasserstein Distance, Ziv Goldfeld, Kristjan Greenewald, Kengo Kato
- Generative View Synthesis: From Single-view Semantics to Novel-view Images, Tewodros Amberbir Habtegebrial, Varun Jampani, Orazio Gallo, Didier Stricker
- Training Generative Adversarial Networks by Solving Ordinary Differential Equations, Chongli Qin, Yan Wu, Jost Tobias Springenberg, Andy Brock, Jeff Donahue, Timothy Lillicrap, Pushmeet Kohli
- Woodbury Transformations for Deep Generative Flows, You Lu, Bert Huang
- Multimodal Generative Learning Utilizing Jensen-Shannon-Divergence, Thomas Sutter, Imant Daunhawer, Julia Vogt
- Exemplar VAE: Linking Generative Models, Nearest Neighbor Retrieval, and Data Augmentation, Sajad Norouzi, David J. Fleet, Mohammad Norouzi
- A Decentralized Parallel Algorithm for Training Generative Adversarial Nets, Mingrui Liu, Wei Zhang, Youssef Mroueh, Xiaodong Cui, Jarret Ross, Tianbao Yang, Payel Das
- Sample-Efficient Optimization in the Latent Space of Deep Generative Models via Weighted Retraining, Austin Tripp, Erik Daxberger, José Miguel Hernández-Lobato
- Training Generative Adversarial Networks with Limited Data, Tero Karras, Miika Aittala, Janne Hellsten, Samuli Laine, Jaakko Lehtinen, Timo Aila
- Improved Techniques for Training Score-Based Generative Models, Yang Song, Stefano Ermon
- Robust Optimal Transport with Applications in Generative Modeling and Domain Adaptation, Yogesh Balaji, Rama Chellappa, Soheil Feizi
- DeepSVG: A Hierarchical Generative Network for Vector Graphics Animation, Alexandre Carlier, Martin Danelljan, Alexandre Alahi, Radu Timofte
- Efficient Learning of Generative Models via Finite-Difference Score Matching, Tianyu Pang, Kun Xu, Chongxuan LI, Yang Song, Stefano Ermon, Jun Zhu
- GRAF: Generative Radiance Fields for 3D-Aware Image Synthesis, Katja Schwarz, Yiyi Liao, Michael Niemeyer, Andreas Geiger
- Learning Semantic-aware Normalization for Generative Adversarial Networks, Heliang Zheng, Jianlong Fu, Yanhong Zeng, Jiebo Luo, Zheng-Jun Zha
- Goal-directed Generation of Discrete Structures with Conditional Generative Models, Amina Mollaysa, Brooks Paige, Alexandros Kalousis
- Lightweight Generative Adversarial Networks for Text-Guided Image Manipulation, Bowen Li, Xiaojuan Qi, Philip Torr, Thomas Lukasiewicz
- Hierarchical Quantized Autoencoders, Will Williams, Sam Ringer, Tom Ash, David MacLeod, Jamie Dougherty, John Hughes
- Autoregressive Score Matching, Chenlin Meng, Lantao Yu, Yang Song, Jiaming Song, Stefano Ermon
- Implicit Rank-Minimizing Autoencoder, Li Jing, Jure Zbontar, yann lecun
- The Autoencoding Variational Autoencoder, Taylan Cemgil, Sumedh Ghaisas, Krishnamurthy Dvijotham, Sven Gowal, Pushmeet Kohli
- Autoencoders that don't overfit towards the Identity, Harald Steck
- NVAE: A Deep Hierarchical Variational Autoencoder, Arash Vahdat, Jan Kautz
- Likelihood Regret: An Out-of-Distribution Detection Score For Variational Auto-encoder, Zhisheng Xiao, Qing Yan, Yali Amit
- Compositional Visual Generation with Energy Based Models, Yilun Du, Shuang Li, Igor Mordatch
- Strictly Batch Imitation Learning by Energy-based Distribution Matching, Daniel Jarrett, Ioana Bica, Mihaela van der Schaar
- Bi-level Score Matching for Learning Energy-based Latent Variable Models, Fan Bao, Chongxuan LI, Kun Xu, Hang Su, Jun Zhu, Bo Zhang
- Learning Latent Space Energy-Based Prior Model, Bo Pang, Tian Han, Erik Nijkamp, Song-Chun Zhu, Ying Nian Wu
- Do 2D GANs Know 3D Shape? Unsupervised 3D Shape Reconstruction from 2D Image GANs, Xingang Pan, Bo Dai, Ziwei Liu, Chen Change Loy, Ping Luo
- Score-Based Generative Modeling through Stochastic Differential Equations, Yang Song, Jascha Sohl-Dickstein, Diederik P Kingma, Abhishek Kumar, Stefano Ermon, Ben Poole
- Image GANs meet Differentiable Rendering for Inverse Graphics and Interpretable 3D Neural Rendering, Yuxuan Zhang, Wenzheng Chen, Huan Ling, Jun Gao, Yinan Zhang, Antonio Torralba, Sanja Fidler
- Improved Autoregressive Modeling with Distribution Smoothing, Chenlin Meng, Jiaming Song, Yang Song, Shengjia Zhao, Stefano Ermon
- VAEBM: A Symbiosis between Variational Autoencoders and Energy-based Models, Zhisheng Xiao, Karsten Kreis, Jan Kautz, Arash Vahdat
- Large Scale Image Completion via Co-Modulated Generative Adversarial Networks, Shengyu Zhao, Jonathan Cui, Yilun Sheng, Yue Dong, Xiao Liang, Eric I-Chao Chang, Yan Xu
- Very Deep VAEs Generalize Autoregressive Models and Can Outperform Them on Images, Rewon Child
- A Good Image Generator Is What You Need for High-Resolution Video Synthesis, Yu Tian, Jian Ren, Menglei Chai, Kyle Olszewski, Xi Peng, Dimitris N. Metaxas, Sergey Tulyakov
- GAN "Steerability" without optimization, Nurit Spingarn, Ron Banner, Tomer Michaeli
- Contrastive Divergence Learning is a Time Reversal Adversarial Game, Omer Yair, Tomer Michaeli
- Influence Estimation for Generative Adversarial Networks, Naoyuki Terashita, Hiroki Ohashi, Yuichi Nonaka, Takashi Kanemaru
- Distributional Sliced-Wasserstein and Applications to Generative Modeling, Khai Nguyen, Nhat Ho, Tung Pham, Hung Bui
- Disentangled Recurrent Wasserstein Autoencoder, Jun Han, Martin Renqiang Min, Ligong Han, Li Erran Li, Xuan Zhang
- On Self-Supervised Image Representations for GAN Evaluation, Stanislav Morozov, Andrey Voynov, Artem Babenko
- Training GANs with Stronger Augmentations via Contrastive Discriminator, Jongheon Jeong, Jinwoo Shin
- Enjoy Your Editing: Controllable GANs for Image Editing via Latent Space Navigation, Peiye Zhuang, Oluwasanmi O Koyejo, Alex Schwing
- Using latent space regression to analyze and leverage compositionality in GANs, Lucy Chai, Jonas Wulff, Phillip Isola
- GANs Can Play Lottery Tickets Too, Xuxi Chen, Zhenyu Zhang, Yongduo Sui, Tianlong Chen
- CcGAN: Continuous Conditional Generative Adversarial Networks for Image Generation, Xin Ding, Yongwei Wang, Zuheng Xu, William J Welch, Z. Jane Wang
- Towards Faster and Stabilized GAN Training for High-fidelity Few-shot Image Synthesis, Bingchen Liu, Yizhe Zhu, Kunpeng Song, Ahmed Elgammal
- DINO: A Conditional Energy-Based GAN for Domain Translation, Konstantinos Vougioukas, Stavros Petridis, Maja Pantic
- Private Post-GAN Boosting, Marcel Neunhoeffer, Steven Wu, Cynthia Dwork
- GAN2GAN: Generative Noise Learning for Blind Denoising with Single Noisy Images, Sungmin Cha, Taeeon Park, Byeongjoon Kim, Jongduk Baek, Taesup Moon
- Taming GANs with Lookahead-Minmax, Tatjana Chavdarova, Matteo Pagliardini, Sebastian U Stich, François Fleuret, Martin Jaggi
- Wasserstein-2 Generative Networks, Alexander Korotin, Vage Egiazarian, Arip Asadulaev, Alexander Safin, Evgeny Burnaev
- Learning Energy-Based Generative Models via Coarse-to-Fine Expanding and Sampling, Yang Zhao, Jianwen Xie, Ping Li
- Generative Time-series Modeling with Fourier Flows, Ahmed Alaa, Alex James Chan, Mihaela van der Schaar
- Conditional Generative Modeling via Learning the Latent Space, Sameera Ramasinghe, Kanchana Nisal Ranasinghe, Salman Khan, Nick Barnes, Stephen Gould
- not-MIWAE: Deep Generative Modelling with Missing not at Random Data, Niels Bruun Ipsen, Pierre-Alexandre Mattei, Jes Frellsen
- Private Image Reconstruction from System Side Channels Using Generative Models, Yuanyuan Yuan, Shuai Wang, Junping Zhang
- Counterfactual Generative Networks, Axel Sauer, Andreas Geiger
- Learning to Generate 3D Shapes with Generative Cellular Automata, Dongsu Zhang, Changwoon Choi, Jeonghwan Kim, Young Min Kim
- Reducing the Computational Cost of Deep Generative Models with Binary Neural Networks, Thomas Bird, Friso Kingma, David Barber
- Group Equivariant Generative Adversarial Networks, Neel Dey, Antong Chen, Soheil Ghafurian
- Refining Deep Generative Models via Discriminator Gradient Flow, Abdul Fatir Ansari, Ming Liang Ang, Harold Soh
- Spatial Dependency Networks: Neural Layers for Improved Generative Image Modeling, Đorđe Miladinović, Aleksandar Stanić, Stefan Bauer, Jürgen Schmidhuber, Joachim M. Buhmann
- Relating by Contrasting: A Data-efficient Framework for Multimodal Generative Models, Yuge Shi, Brooks Paige, Philip Torr, Siddharth N
- Unsupervised Meta-Learning through Latent-Space Interpolation in Generative Models, Siavash Khodadadeh, Sharare Zehtabian, Saeed Vahidian, Weijia Wang, Bill Lin, Ladislau Boloni
- Flowtron: an Autoregressive Flow-based Generative Network for Text-to-Speech Synthesis, Rafael Valle, Kevin J. Shih, Ryan Prenger, Bryan Catanzaro
- Understanding Over-parameterization in Generative Adversarial Networks, Yogesh Balaji, Mohammadmahdi Sajedi, Neha Mukund Kalibhat, Mucong Ding, Dominik Stöger, Mahdi Soltanolkotabi, Soheil Feizi
- Evaluating the Disentanglement of Deep Generative Models through Manifold Topology, Sharon Zhou, Eric Zelikman, Fred Lu, Andrew Y. Ng, Gunnar E. Carlsson, Stefano Ermon
- Decentralized Attribution of Generative Models, Changhoon Kim, Yi Ren, Yezhou Yang
- Bowtie Networks: Generative Modeling for Joint Few-Shot Recognition and Novel-View Synthesis, Zhipeng Bao, Yu-Xiong Wang, Martial Hebert
- A Geometric Analysis of Deep Generative Image Models and Its Applications, Binxu Wang, Carlos R Ponce
- Unsupervised Audiovisual Synthesis via Exemplar Autoencoders, Kangle Deng, Aayush Bansal, Deva Ramanan
- Property Controllable Variational Autoencoder via Invertible Mutual Dependence, Xiaojie Guo, Yuanqi Du, Liang Zhao
- Anytime Sampling for Autoregressive Models via Ordered Autoencoding, Yilun Xu, Yang Song, Sahaj Garg, Linyuan Gong, Rui Shu, Aditya Grover, Stefano Ermon
- Tomographic Auto-Encoder: Unsupervised Bayesian Recovery of Corrupted Data, Francesco Tonolini, Pablo Garcia Moreno, Andreas Damianou, Roderick Murray-Smith
- Uncertainty Principles of Encoding GANs, Ruili Feng, Zhouchen Lin, Jiapeng Zhu, Deli Zhao, Jingren Zhou, Zheng-Jun Zha
- Understanding Noise Injection in GANs, Ruili Feng, Deli Zhao, Zheng-Jun Zha
- Pareto GAN: Extending the Representational Power of GANs to Heavy-Tailed Distributions, Todd Huster, Jeremy Cohen, Zinan Lin, Kevin Chan, Charles Kamhoua, Nandi O. Leslie, Cho-Yu Jason Chiang, Vyas Sekar
- Functional Space Analysis of Local GAN Convergence, Valentin Khrulkov, Artem Babenko, Ivan Oseledets
- Neural SDEs as Infinite-Dimensional GANs, Patrick Kidger, James Foster, Xuechen Li, Terry J Lyons
- WGAN with an Infinitely Wide Generator Has No Spurious Stationary Points, Albert No, Taeho Yoon, Kwon Sehyun, Ernest K Ryu
- On Characterizing GAN Convergence Through Proximal Duality Gap, Sahil Sidheekh, Aroof Aimen, Narayanan C Krishnan
- Generative Adversarial Transformers, Drew A Hudson, Larry Zitnick
- Provable Lipschitz Certification for Generative Models, Matt Jordan, Alex Dimakis
- Prior Image-Constrained Reconstruction using Style-Based Generative Models, Varun A Kelkar, Mark Anastasio
- NeRF-VAE: A Geometry Aware 3D Scene Generative Model, Adam R Kosiorek, Heiko Strathmann, Daniel Zoran, Pol Moreno, Rosalia Schneider, Sona Mokra, Danilo Jimenez Rezende
- Run-Sort-ReRun: Escaping Batch Size Limitations in Sliced Wasserstein Generative Models, Jose Lezama, Wei Chen, Qiang Qiu
- Generative Adversarial Networks for Markovian Temporal Dynamics: Stochastic Continuous Data Generation, Sung Woo Park, Dong Wook Shu, Junseok Kwon
- A Language for Counterfactual Generative Models, Zenna Tavares, James Koppel, Xin Zhang, Ria Das, Armando Solar-Lezama
- Deep Generative Learning via Schrödinger Bridge, Gefei Wang, Yuling Jiao, Qian Xu, Yang Wang, Can Yang
- A Unified Generative Adversarial Network Training via Self-Labeling and Self-Attention, Tomoki Watanabe, Paolo Favaro
- Adversarial Purification with Score-based Generative Models, Jongmin Yoon, Sung Ju Hwang, Juho Lee
- Understanding Failures in Out-of-Distribution Detection with Deep Generative Models, Lily Zhang, Mark Goldstein, Rajesh Ranganath
- Multiscale Invertible Generative Networks for High-Dimensional Bayesian Inference, Shumao Zhang, Pengchuan Zhang, Thomas Y Hou
- Unified Robust Semi-Supervised Variational Autoencoder, Xu Chen
- Conditional Variational Autoencoder with Adversarial Learning for End-to-End Text-to-Speech, Jaehyeon Kim, Jungil Kong, Juhee Son
- MorphVAE: Generating Neural Morphologies from 3D-Walks using a Variational Autoencoder with Spherical Latent Space, Sophie C. Laturnus, Philipp Berens
- Autoencoder Image Interpolation by Shaping the Latent Space, Alon Oring, Zohar Yakhini, Yacov Hel-Or
- Spectral Smoothing Unveils Phase Transitions in Hierarchical Variational Autoencoders, Adeel Pervez, Efstratios Gavves
- Autoencoding Under Normalization Constraints, Sangwoong Yoon, Yung-Kyun Noh, Frank Park
- Learning from Nested Data with Ornstein Auto-Encoders, Youngwon Choi, Sungdong Lee, Joong-Ho Won
- Monte Carlo Variational Auto-Encoders, Achille Thin, Nikita Kotelevskii, Arnaud Doucet, Alain Durmus, Eric Moulines, Maxim Panov
- Zero-Shot Text-to-Image Generation, Aditya Ramesh, Mikhail Pavlov, Gabriel Goh, Scott Gray, Chelsea Voss, Alec Radford, Mark Chen, Ilya Sutskever
NeurIPS 2021 (OpenReview)
- Moser Flow: Divergence-based Generative Modeling on Manifolds, Noam Rozen, Aditya Grover, Maximilian Nickel, Yaron Lipman
- Differentiable Quality Diversity, Matthew Christopher Fontaine, Stefanos Nikolaidis
- Alias-Free Generative Adversarial Networks, Tero Karras, Miika Aittala, Samuli Laine, Erik Härkönen, Janne Hellsten, Jaakko Lehtinen, Timo Aila
- Breaking the Dilemma of Medical Image-to-image Translation, Lingke Kong, Chenyu Lian, Detian Huang, ZhenJiang Li, Yanle Hu, Qichao Zhou
- Diffusion Schrödinger Bridge with Applications to Score-Based Generative Modeling, Valentin De Bortoli, James Thornton, Jeremy Heng, Arnaud Doucet
- Statistical Regeneration Guarantees of the Wasserstein Autoencoder with Latent Space Consistency, Anish Chakrabarty, Swagatam Das
- On the Value of Infinite Gradients in Variational Autoencoder Models, Bin Dai, Li Kevin Wenliang, David Wipf
- Maximum Likelihood Training of Score-Based Diffusion Models, Yang Song, Conor Durkan, Iain Murray, Stefano Ermon
- A Variational Perspective on Diffusion-Based Generative Models and Score Matching, Chin-Wei Huang, Jae Hyun Lim, Aaron Courville
- Diffusion Models Beat GANs on Image Synthesis, Prafulla Dhariwal, Alexander Quinn Nichol
- Instance-Conditioned GAN, Arantxa Casanova, Marlene Careil, Jakob Verbeek, Michal Drozdzal, Adriana Romero
- Self-Supervised GANs with Label Augmentation, Liang Hou, Huawei Shen, Qi Cao, Xueqi Cheng
- Projected GANs Converge Faster, Axel Sauer, Kashyap Chitta, Jens Müller, Andreas Geiger
- Data-Efficient GAN Training Beyond (Just) Augmentations: A Lottery Ticket Perspective, Tianlong Chen, Yu Cheng, Zhe Gan, Jingjing Liu, Zhangyang Wang
- Invertible Tabular GANs: Killing Two Birds with One Stone for Tabular Data Synthesis, JAEHOON LEE, Jihyeon Hyeong, Jinsung Jeon, Noseong Park, Jihoon Cho
- Non-asymptotic Error Bounds for Bidirectional GANs, Shiao Liu, Yunfei Yang, Jian Huang, Yuling Jiao, Yang Wang
- Why Spectral Normalization Stabilizes GANs: Analysis and Improvements, Zinan Lin, Vyas Sekar, Giulia Fanti
- CAM-GAN: Continual Adaptation Modules for Generative Adversarial Networks, Sakshi Varshney, Vinay Kumar Verma, Srijith P K, Lawrence Carin, Piyush Rai
- Self-Diagnosing GAN: Diagnosing Underrepresented Samples in Generative Adversarial Networks, Jinhee Lee, Haeri Kim, Youngkyu Hong, Hye Won Chung
- Particle Cloud Generation with Message Passing Generative Adversarial Networks, Raghav Kansal, Javier Duarte, Hao Su, Breno Orzari, Thiago R F P Tomei, Maurizio Pierini, Mary Touranakou, Jean-roch Vlimant, Dimitrios Gunopulos
- TransGAN: Two Pure Transformers Can Make One Strong GAN, and That Can Scale Up, Yifan Jiang, Shiyu Chang, Zhangyang Wang
- Low-Rank Subspaces in GANs, Jiapeng Zhu, Ruili Feng, Yujun Shen, Deli Zhao, Zheng-Jun Zha, Jingren Zhou, Qifeng Chen
- Lip to Speech Synthesis with Visual Context Attentional GAN, Minsu Kim, Joanna Hong, Yong Man Ro
- Revisiting Discriminator in GAN Compression: A Generator-discriminator Cooperative Compression Scheme, ShaoJie Li, Jie Wu, Xuefeng Xiao, Fei Chao, Xudong Mao, Rongrong Ji
- EditGAN: High-Precision Semantic Image Editing, Huan Ling, Karsten Kreis, Daiqing Li, Seung Wook Kim, Antonio Torralba, Sanja Fidler
- Rebooting ACGAN: Auxiliary Classifier GANs with Stable Training, Minguk Kang, Woohyeon Joseph Shim, Minsu Cho, Jaesik Park
- Rethinking Conditional GAN Training: An Approach using Geometrically Structured Latent Manifolds, Sameera Ramasinghe, Moshiur R Farazi, Salman Khan, Nick Barnes, Stephen Gould
- BlendGAN: Implicitly GAN Blending for Arbitrary Stylized Face Generation, Mingcong Liu, Qiang Li, Zekui Qin, Guoxin Zhang, Pengfei Wan, Wen Zheng
- Data-Efficient Instance Generation from Instance Discrimination, Ceyuan Yang, Yujun Shen, Yinghao Xu, Bolei Zhou
- On the Frequency Bias of Generative Models, Katja Schwarz, Yiyi Liao, Andreas Geiger
- Improved Transformer for High-Resolution GANs, Long Zhao, Zizhao Zhang, Ting Chen, Dimitris N. Metaxas, Han Zhang
- Deceive D: Adaptive Pseudo Augmentation for GAN Training with Limited Data, Liming Jiang, Bo Dai, Wayne Wu, Chen Change Loy
- Towards Scalable Unpaired Virtual Try-On via Patch-Routed Spatially-Adaptive GAN, Zhenyu Xie, Zaiyu Huang, Fuwei Zhao, Haoye Dong, Michael Kampffmeyer, Xiaodan Liang
- CogView: Mastering Text-to-Image Generation via Transformers, Ming Ding, Zhuoyi Yang, Wenyi Hong, Wendi Zheng, Chang Zhou, Da Yin, Junyang Lin, Xu Zou, Zhou Shao, Hongxia Yang, Jie Tang
- Improving Visual Quality of Image Synthesis by A Token-based Generator with Transformers, Yanhong Zeng, Huan Yang, Hongyang Chao, Jianbo Wang, Jianlong Fu
- Manifold Topology Divergence: a Framework for Comparing Data Manifolds, Serguei Barannikov, Ilya Trofimov, Grigorii Sotnikov, Ekaterina Trimbach, Alexander Korotin, Alexander Filippov, Evgeny Burnaev
- DECAF: Generating Fair Synthetic Data Using Causally-Aware Generative Networks, Boris van Breugel, Trent Kyono, Jeroen Berrevoets, Mihaela van der Schaar
- A Unified View of cGANs with and without Classifiers, Si-An Chen, Chun-Liang Li, Hsuan-Tien Lin
- Conditional Generation Using Polynomial Expansions, Grigorios Chrysos, Markos Georgopoulos, Yannis Panagakis
- Generative Occupancy Fields for 3D Surface-Aware Image Synthesis, Xudong Xu, Xingang Pan, Dahua Lin, Bo Dai
- Implicit Generative Copulas, Tim Janke, Mohamed Ghanmi, Florian Steinke
- Don’t Generate Me: Training Differentially Private Generative Models with Sinkhorn Divergence, Tianshi Cao, Alex Bie, Arash Vahdat, Sanja Fidler, Karsten Kreis
- Learning Generative Vision Transformer with Energy-Based Latent Space for Saliency Prediction, Jing Zhang, Jianwen Xie, Nick Barnes, Ping Li
- Divergence Frontiers for Generative Models: Sample Complexity, Quantization Effects, and Frontier Integrals, Lang Liu, Krishna Pillutla, Sean Welleck, Sewoong Oh, Yejin Choi, Zaid Harchaoui
- On the Generative Utility of Cyclic Conditionals, Chang Liu, Haoyue Tang, Tao Qin, Jintao Wang, Tie-Yan Liu
- Score-based Generative Neural Networks for Large-Scale Optimal Transport, Max Daniels, Tyler Maunu, PAul HAnd
- Bridging Explicit and Implicit Deep Generative Models via Neural Stein Estimators, Qitian Wu, Rui Gao, Hongyuan Zha
- On Memorization in Probabilistic Deep Generative Models, Gerrit J.J. Van den Burg, Chris Williams
- Score-based Generative Modeling in Latent Space, Arash Vahdat, Karsten Kreis, Jan Kautz
- SketchGen: Generating Constrained CAD Sketches, Wamiq Reyaz Para, Shariq Farooq Bhat, Paul Guerrero, Tom Kelly, Niloy Mitra, Leonidas Guibas, Peter Wonka
- Physics-Integrated Variational Autoencoders for Robust and Interpretable Generative Modeling, Naoya Takeishi, Alexandros Kalousis
- Evidential Softmax for Sparse Multimodal Distributions in Deep Generative Models, Phil Chen, Masha Itkina, Ransalu Senanayake, Mykel Kochenderfer
- Flow Network based Generative Models for Non-Iterative Diverse Candidate Generation, Emmanuel Bengio, Moksh Jain, Maksym Korablyov, Doina Precup, Yoshua Bengio
- Improving Robustness using Generated Data, Sven Gowal, Sylvestre-Alvise Rebuffi, Olivia Wiles, Florian Stimberg, Dan Andrei Calian, Timothy Mann
- D2C: Diffusion-Decoding Models for Few-Shot Conditional Generation, Abhishek Sinha, Jiaming Song, Chenlin Meng, Stefano Ermon
- Shape your Space: A Gaussian Mixture Regularization Approach to Deterministic Autoencoders, Amrutha Saseendran, Kathrin Skubch, Stefan Falkner, Margret Keuper
- On Density Estimation with Diffusion Models, Diederik P Kingma, Tim Salimans, Ben Poole, Jonathan Ho
- Fast Approximation of the Sliced-Wasserstein Distance Using Concentration of Random Projections, Kimia Nadjahi, Alain Durmus, Pierre Jacob, Roland Badeau, Umut Simsekli
- PortaSpeech: Portable and High-Quality Generative Text-to-Speech, Yi Ren, Jinglin Liu, Zhou Zhao
- Locally Most Powerful Bayesian Test for Out-of-Distribution Detection using Deep Generative Models, Keunseo Kim, JunCheol Shin, Heeyoung Kim
- Topographic VAEs learn Equivariant Capsules, T. Anderson Keller, Max Welling
- ByPE-VAE: Bayesian Pseudocoresets Exemplar VAE, Qingzhong Ai, LIRONG HE, SHIYU LIU, Zenglin Xu
- Understanding Instance-based Interpretability of Variational Auto-Encoders, Zhifeng Kong, Kamalika Chaudhuri
- Local Disentanglement in Variational Auto-Encoders Using Jacobian $L_1$ Regularization, Travers Rhodes, Daniel Lee
- Consistency Regularization for Variational Auto-Encoders, Samarth Sinha, Adji Bousso Dieng