PacGAN: The Power of Two Samples in Generative Adversarial Networks
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Ashish Khetan | Sewoong Oh | Zinan Lin | Giulia C. Fanti | G. Fanti | Sewoong Oh | Ashish Khetan | Zinan Lin | A. Khetan
[1] D. Blackwell. Equivalent Comparisons of Experiments , 1953 .
[2] A. J. Stam. Some Inequalities Satisfied by the Quantities of Information of Fisher and Shannon , 1959, Inf. Control..
[3] T. Cover,et al. Determinant inequalities via information theory , 1988 .
[4] Amir Dembo,et al. Information theoretic inequalities , 1991, IEEE Trans. Inf. Theory.
[5] Ram Zamir,et al. A Proof of the Fisher Information Inequality via a Data Processing Argument , 1998, IEEE Trans. Inf. Theory.
[6] Yann LeCun,et al. The mnist database of handwritten digits , 2005 .
[7] Sergio Verdú,et al. A simple proof of the entropy-power inequality , 2006, IEEE Transactions on Information Theory.
[8] Tie Liu,et al. An Extremal Inequality Motivated by Multiterminal Information-Theoretic Problems , 2006, IEEE Transactions on Information Theory.
[9] Alex Krizhevsky,et al. Learning Multiple Layers of Features from Tiny Images , 2009 .
[10] Bernhard Schölkopf,et al. Hilbert Space Embeddings and Metrics on Probability Measures , 2009, J. Mach. Learn. Res..
[11] Geoffrey E. Hinton. A Practical Guide to Training Restricted Boltzmann Machines , 2012, Neural Networks: Tricks of the Trade.
[12] Jeffrey Dean,et al. Distributed Representations of Words and Phrases and their Compositionality , 2013, NIPS.
[13] Yoshua Bengio,et al. Generative Adversarial Nets , 2014, NIPS.
[14] Max Welling,et al. Auto-Encoding Variational Bayes , 2013, ICLR.
[15] Nancy Wilkins-Diehr,et al. XSEDE: Accelerating Scientific Discovery , 2014, Computing in Science & Engineering.
[16] Pramod Viswanath,et al. Extremal Mechanisms for Local Differential Privacy , 2014, J. Mach. Learn. Res..
[17] Rob Fergus,et al. Deep Generative Image Models using a Laplacian Pyramid of Adversarial Networks , 2015, NIPS.
[18] Sergey Ioffe,et al. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift , 2015, ICML.
[19] Xiaogang Wang,et al. Deep Learning Face Attributes in the Wild , 2014, 2015 IEEE International Conference on Computer Vision (ICCV).
[20] Ralph Roskies,et al. Bridges: a uniquely flexible HPC resource for new communities and data analytics , 2015, XSEDE.
[21] Jimmy Ba,et al. Adam: A Method for Stochastic Optimization , 2014, ICLR.
[22] Pramod Viswanath,et al. Secure Multi-party Differential Privacy , 2015, NIPS.
[23] Matthias Bethge,et al. A note on the evaluation of generative models , 2015, ICLR.
[24] Wojciech Zaremba,et al. Improved Techniques for Training GANs , 2016, NIPS.
[25] Bernt Schiele,et al. Generative Adversarial Text to Image Synthesis , 2016, ICML.
[26] Soumith Chintala,et al. Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks , 2015, ICLR.
[27] Sergey Levine,et al. A Connection between Generative Adversarial Networks, Inverse Reinforcement Learning, and Energy-Based Models , 2016, ArXiv.
[28] Xavier Bresson,et al. Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering , 2016, NIPS.
[29] Antonio Torralba,et al. Generating Videos with Scene Dynamics , 2016, NIPS.
[30] Sebastian Nowozin,et al. f-GAN: Training Generative Neural Samplers using Variational Divergence Minimization , 2016, NIPS.
[31] A. G. Wilson,et al. Bayesian GANs , 2017, NIPS 2017.
[32] Trevor Darrell,et al. Adversarial Feature Learning , 2016, ICLR.
[33] J. Zico Kolter,et al. Gradient descent GAN optimization is locally stable , 2017, NIPS.
[34] Sepp Hochreiter,et al. GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium , 2017, NIPS.
[35] Lantao Yu,et al. SeqGAN: Sequence Generative Adversarial Nets with Policy Gradient , 2016, AAAI.
[36] Isaac Tamblyn,et al. Phase space sampling and operator confidence with generative adversarial networks , 2017, 1710.08053.
[37] Ian J. Goodfellow,et al. NIPS 2016 Tutorial: Generative Adversarial Networks , 2016, ArXiv.
[38] Alexandros G. Dimakis,et al. The Robust Manifold Defense: Adversarial Training using Generative Models , 2017, ArXiv.
[39] Léon Bottou,et al. Wasserstein GAN , 2017, ArXiv.
[40] Max Welling,et al. Semi-Supervised Classification with Graph Convolutional Networks , 2016, ICLR.
[41] Sebastian Nowozin,et al. The Numerics of GANs , 2017, NIPS.
[42] Trung Le,et al. Dual Discriminator Generative Adversarial Nets , 2017, NIPS.
[43] David Pfau,et al. Unrolled Generative Adversarial Networks , 2016, ICLR.
[44] Yingyu Liang,et al. Generalization and Equilibrium in Generative Adversarial Nets (GANs) , 2017, ICML.
[45] Sebastian Nowozin,et al. Stabilizing Training of Generative Adversarial Networks through Regularization , 2017, NIPS.
[46] Charles A. Sutton,et al. VEEGAN: Reducing Mode Collapse in GANs using Implicit Variational Learning , 2017, NIPS.
[47] Ruslan Salakhutdinov,et al. On the Quantitative Analysis of Decoder-Based Generative Models , 2016, ICLR.
[48] Pramod Viswanath,et al. The Composition Theorem for Differential Privacy , 2013, IEEE Transactions on Information Theory.
[49] Alexei A. Efros,et al. Image-to-Image Translation with Conditional Adversarial Networks , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[50] Bernhard Schölkopf,et al. AdaGAN: Boosting Generative Models , 2017, NIPS.
[51] Alexander J. Smola,et al. Deep Sets , 2017, 1703.06114.
[52] Richard Nock,et al. f-GANs in an Information Geometric Nutshell , 2017, NIPS.
[53] Fei Xia,et al. Understanding GANs: the LQG Setting , 2017, ArXiv.
[54] Yiming Yang,et al. MMD GAN: Towards Deeper Understanding of Moment Matching Network , 2017, NIPS.
[55] Yi Zhang,et al. Theoretical limitations of Encoder-Decoder GAN architectures , 2017, ArXiv.
[56] Alexander J. Smola,et al. Generative Models and Model Criticism via Optimized Maximum Mean Discrepancy , 2016, ICLR.
[57] Jerry Li,et al. Towards Understanding the Dynamics of Generative Adversarial Networks , 2017, ArXiv.
[58] Christian Ledig,et al. Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[59] Alexandros G. Dimakis,et al. Compressed Sensing using Generative Models , 2017, ICML.
[60] Kamalika Chaudhuri,et al. Approximation and Convergence Properties of Generative Adversarial Learning , 2017, NIPS.
[61] Richard S. Zemel,et al. Dualing GANs , 2017, NIPS.
[62] Aaron C. Courville,et al. Improved Training of Wasserstein GANs , 2017, NIPS.
[63] Aleksander Madry,et al. A Classification-Based Perspective on GAN Distributions , 2017, ArXiv.
[64] Yoshua Bengio,et al. Mode Regularized Generative Adversarial Networks , 2016, ICLR.
[65] Yi Zhang,et al. Do GANs actually learn the distribution? An empirical study , 2017, ArXiv.
[66] Aaron C. Courville,et al. Adversarially Learned Inference , 2016, ICLR.
[67] Chong Wang,et al. Attention-based Graph Neural Network for Semi-supervised Learning , 2018, ArXiv.
[68] Prafulla Dhariwal,et al. Glow: Generative Flow with Invertible 1x1 Convolutions , 2018, NeurIPS.
[69] Kamalika Chaudhuri,et al. The Inductive Bias of Restricted f-GANs , 2018, ArXiv.
[70] Jaakko Lehtinen,et al. Progressive Growing of GANs for Improved Quality, Stability, and Variation , 2017, ICLR.
[71] Yuichi Yoshida,et al. Spectral Normalization for Generative Adversarial Networks , 2018, ICLR.
[72] Arthur Gretton,et al. Demystifying MMD GANs , 2018, ICLR.
[73] Alexandros G. Dimakis,et al. AmbientGAN: Generative models from lossy measurements , 2018, ICLR.
[74] Yair Weiss,et al. On GANs and GMMs , 2018, NeurIPS.