429 citations · 633 across the 15 of their papers we have counts for
5 papers · 1 filter
Semi-Supervised Learning with IPM-based GANs: an Empirical Study
Tom Sercu, Youssef Mroueh
We present an empirical investigation of a recent class of Generative Adversarial Networks (GANs) using Integral Probability Metrics (IPM) and their performance for semi-supervised…
Sobolev GAN
Youssef Mroueh, Chun-Liang Li, Tom Sercu +2
We propose a new Integral Probability Metric (IPM) between distributions: the Sobolev IPM. The Sobolev IPM compares the mean discrepancy of two distributions for functions (critic)…
Fisher GAN
Youssef Mroueh, Tom Sercu
Generative Adversarial Networks (GANs) are powerful models for learning complex distributions. Stable training of GANs has been addressed in many recent works which explore differe…
English Conversational Telephone Speech Recognition by Humans and Machines
George Saon, Gakuto Kurata, Tom Sercu +9
One of the most difficult speech recognition tasks is accurate recognition of human to human communication. Advances in deep learning over the last few years have produced major sp…
McGan: Mean and Covariance Feature Matching GAN
Youssef Mroueh, Tom Sercu, Vaibhava Goel
We introduce new families of Integral Probability Metrics (IPM) for training Generative Adversarial Networks (GAN). Our IPMs are based on matching statistics of distributions embed…