1k citations · 2.4k across the 12 of their papers we have counts for
10 papers · 1 filter
Discriminator Rejection Sampling
Samaneh Azadi, Catherine Olsson, Trevor Darrell +2
We propose a rejection sampling scheme using the discriminator of a GAN to approximately correct errors in the GAN generator distribution. We show that under quite strict assumptio…
Unrestricted Adversarial Examples
Tom B. Brown, Nicholas Carlini, Chiyuan Zhang +3
We introduce a two-player contest for evaluating the safety and robustness of machine learning systems, with a large prize pool. Unlike most prior work in ML robustness, which stud…
Skill Rating for Generative Models
Catherine Olsson, Surya Bhupatiraju, Tom Brown +2
We explore a new way to evaluate generative models using insights from evaluation of competitive games between human players. We show experimentally that tournaments between genera…
TensorFuzz: Debugging Neural Networks with Coverage-Guided Fuzzing
Augustus Odena, Ian Goodfellow
Machine learning models are notoriously difficult to interpret and debug. This is particularly true of neural networks. In this work, we introduce automated software testing techni…
Self-Attention Generative Adversarial Networks
Han Zhang, Ian Goodfellow, Dimitris Metaxas +1
In this paper, we propose the Self-Attention Generative Adversarial Network (SAGAN) which allows attention-driven, long-range dependency modeling for image generation tasks. Tradit…
MaskGAN: Better Text Generation via Filling in the______
William Fedus, Ian Goodfellow, Andrew M. Dai
Neural text generation models are often autoregressive language models or seq2seq models. These models generate text by sampling words sequentially, with each word conditioned on t…