Training Deep Energy-Based Models with f-Divergence Minimization

6 March 2020

Papers citing "Training Deep Energy-Based Models with f-Divergence Minimization"

9 / 9 papers shown

Title
Entropy-driven Unsupervised Keypoint Representation Learning in Videos A. Younes Simone Schaub-Meyer Georgia Chalvatzaki SSL 29 0 0 30 Sep 2022
Deep Generative Modelling: A Comparative Review of VAEs, GANs, Normalizing Flows, Energy-Based and Autoregressive Models Sam Bond-Taylor Adam Leach Yang Long Chris G. Willcocks VLM TPM 36 478 0 08 Mar 2021
EBMs Trained with Maximum Likelihood are Generator Models Trained with a Self-adverserial Loss Zhisheng Xiao Qing Yan Y. Amit 29 2 0 23 Feb 2021
How to Train Your Energy-Based Models Yang Song Diederik P. Kingma DiffM 24 241 0 09 Jan 2021
Improved Contrastive Divergence Training of Energy Based Models Yilun Du Shuang Li J. Tenenbaum Igor Mordatch 27 138 0 02 Dec 2020
A Neural Network MCMC sampler that maximizes Proposal Entropy Zengyi Li Yubei Chen Friedrich T. Sommer 25 14 0 07 Oct 2020
$f$ -GAIL: Learning $f$ -Divergence for Generative Adversarial Imitation Learning Xin Zhang Yanhua Li Ziming Zhang Zhi-Li Zhang 19 31 0 02 Oct 2020
Conditional Image Synthesis With Auxiliary Classifier GANs Augustus Odena C. Olah Jonathon Shlens GAN 238 3,190 0 30 Oct 2016
Pixel Recurrent Neural Networks Aaron van den Oord Nal Kalchbrenner Koray Kavukcuoglu SSeg GAN 233 2,545 0 25 Jan 2016