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This paper explores the problem of generative modeling, aiming to simulate diverse examples from an unknown distribution based on observed examples.
The Speed of Mean Glivenko-Cantelli Convergence
Richard M. Dudley · 1969
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On Lipschitz Embedding of Finite Metric Spaces in Hilbert space
Jean Bourgain · 1985
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The MNIST Database of Handwritten Digits
Yann LeCun · 1998
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Learning Multiple Layers of Features from Tiny Images
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Sample Complexity of Testing the Manifold Hypothesis
Hariharan Narayanan and Sanjoy K. Mitter · 2010
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On the Mean Speed of Convergence of Empirical and Occupation Measures in Wasserstein Distance
Emmanuel Boissard and Thibaut Le Gouic · 2014
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Generative Adversarial Networks
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2014
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Auto-Encoding Variational Bayes
Diederik P. Kingma and Max Welling · 2014
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Optimal Transport: Theory and Applications
Yann Ollivier, Hervé Pajot, and Cedric Villani · 2014
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Testing the Manifold Hypothesis
Charles Fefferman, Sanjoy Mitter, and Hariharan Narayanan · 2016
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Wasserstein Generative Adversarial Networks
Martin Arjovsky, Soumith Chintala, and Léon Bottou · 2017
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Generalization and Equilibrium in Generative Adversarial Nets (GANs)
Sanjeev Arora, Rong Ge, Yingyu Liang, Tengyu Ma, and Yi Zhang · 2017
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Adversarially Learned Inference
Vincent Dumoulin, Ishmael Belghazi, Ben Poole, Alex Lamb, Martín Arjovsky, Olivier Mastropietro, and Aaron C. Courville · 2017
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Improved Training of Wasserstein GANs
Ishaan Gulrajani, Faruk Ahmed, Martín Arjovsky, Vincent Dumoulin, and Aaron C. Courville · 2017
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Medical Image Synthesis with Context-Aware Generative Adversarial Networks
Dong Nie, Roger Trullo, Jun Lian, Caroline Petitjean, Su Ruan, Qian Wang, and Dinggang Shen · 2017
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Stabilizing Training of Generative Adversarial Networks through Regularization
Kevin Roth, Aurélien Lucchi, Sebastian Nowozin, and Thomas Hofmann · 2017
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VEEGAN: Reducing Mode Collapse in GANs using Implicit Variational Learning
Akash Srivastava, Lazar Valkov, Chris Russell, Michael U. Gutmann, and Charles Sutton · 2017
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Error Bounds for Approximations with Deep ReLU Networks
Dmitry Yarotsky · 2017
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Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
Jun-Yan Zhu, Taesung Park, Phillip Isola, and Alexei A. Efros · 2017
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Do GANs Learn the Distribution? Some Theory and Empirics
Sanjeev Arora, Andrej Risteski, and Yi Zhang · 2018
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Approximability of Discriminators Implies Diversity in GANs
Yu Bai, Tengyu Ma, and Andrej Risteski · 2018
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Learning generative models with sinkhorn divergences
Aude Genevay, Gabriel Peyré, and Marco Cuturi · 2018
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Theoretical Insights into Memorization in GANs
Vaishnavh Nagarajan, Colin Raffel, and Ian J Goodfellow · 2018
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CaloGAN: Simulating 3D High Energy Particle Showers in Multilayer Electromagnetic Calorimeters with Generative Adversarial Networks
Michela Paganini, Luke de Oliveira, and Benjamin Nachman · 2018
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Optimal Approximation of Piecewise Smooth Functions using Deep ReLU Neural Networks
Philipp Petersen and Felix Voigtlaender · 2018
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On the Regularization of Wasserstein GANs
Henning Petzka, Asja Fischer, and Denis Lukovnikov · 2018
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BourGAN: Generative Networks with Metric Embeddings
Chang Xiao, Peilin Zhong, and Changxi Zheng · 2018
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DeepLesion: Automated Mining of Large-Scale Lesion Annotations and Universal Lesion Detection with Deep Learning
Diffusion Schrödinger Bridge with Applications to Score-Based Generative Modeling
Valentin De Bortoli, James Thornton, Jeremy Heng, and Arnaud Doucet · 2021
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Provable Lipschitz Certification for Generative Models
Matt Jordan and Alex Dimakis · 2021
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How Well Generative Adversarial Networks Learn Distributions
Tengyuan Liang · 2021
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Expanding Functional Protein Sequence Spaces using Generative Adversarial Networks
Donatas Repecka, Vykintas Jauniskis, Laurynas Karpus, Elzbieta Rembeza, Irmantas Rokaitis, Jan Zrimec, Simona Poviloniene, Audrius Laurynenas, Sandra Viknander, Wissam Abuajwa, Otto Savolainen, Rolandas Meškys, Martin Engqvist, and Aleksej Zelezniak · 2021
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Statistical Guarantees for Generative Models without Domination
Nicolas Schreuder, Victor-Emmanuel Brunel, and Arnak S. Dalalyan · 2021
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Ke Yan, Xiaosong Wang, Le Lu, and Ronald M Summers · 2018
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Generating Energy Data for Machine Learning with Recurrent Generative Adversarial Networks
Mohammad Navid Fekri, Ananda Mohon Ghosh, and Katarina Grolinger · 2019
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Towards GAN Benchmarks Which require Generalization
Ishaan Gulrajani, Colin Raffel, and Luke Metz · 2019
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Computational Optimal Transport: With Applications to Data Science
Gabriel Peyré and Marco Cuturi · 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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Some Theoretical Properties of GANs
Gérard Biau, Benoît Cadre, Maxime Sangnier, and Ugo Tanielian · 2020
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Generative Modeling with Denoising Auto-Encoders and Langevin Sampling
Adam Block, Youssef Mroueh, and Alexander Rakhlin · 2020
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Valentin De Bortoli, Emile Mathieu, MJ Hutchinson, James Thornton, Yee Whye Teh, and Arnaud Doucet · 2022
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Minimax Optimality (Probably) Doesn’t Imply Distribution Learning for GANs
Sitan Chen, Jerry Li, Yuanzhi Li, and Raghu Meka · 2022
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An Error Analysis of Generative Adversarial Networks for Learning Distributions
Jian Huang, Yuling Jiao, Zhen Li, Shiao Liu, Yang Wang, and Yunfei Yang · 2022
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Estimation of Wasserstein Distances in the Spiked Transport Model
Jonathan Niles-Weed and Philippe Rigollet · 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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Rates of Convergence for Density Estimation with Generative Adversarial Networks, 2023
Denis Belomestny, Eric Moulines, Alexey Naumov, Nikita Puchkin, and Sergey Samsonov · 2023
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Extracting Training Data from Diffusion Models
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A Likelihood Approach to Nonparametric Estimation of a Singular Distribution Using Deep Generative Models
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Ambient Diffusion: Learning Clean Distributions from Corrupted Data
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Measuring Forgetting of Memorized Training Examples
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Mode-Seeking Divergences: Theory and Applications to GANs
Cheuk Ting Li and Farzan Farnia · 2023
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Wasserstein GANs are Minimax Optimal Distribution Estimators, 2023
Arthur Stéphanovitch, Eddie Aamari, and Clément Levrard · 2023
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Minimax Rate of Distribution Estimation on Unknown Submanifold under Adversarial Losses, 2023
Rong Tang and Yun Yang · 2023
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Direct Parameterization of Lipschitz-Bounded Deep Networks
Ruigang Wang and Ian R. Manchester · 2023
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Indeterminacy in Generative Models: Characterization and Strong Identifiability
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Minimax Optimal Density Estimation using a Shallow Generative Model with a One-Dimensional Latent Variable
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A good score does not lead to a good generative model
Sixu Li, Shi Chen, and Qin Li · 2024
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