47 citations · 145 across the 8 of their papers we have counts for
10 papers · 1 filter
Top-k Training of GANs: Improving GAN Performance by Throwing Away Bad Samples
Samarth Sinha, Zhengli Zhao, Anirudh Goyal +2
We introduce a simple (one line of code) modification to the Generative Adversarial Network (GAN) training algorithm that materially improves results with no increase in computatio…
Improved Consistency Regularization for GANs
Zhengli Zhao, Sameer Singh, Honglak Lee +3
Recent work has increased the performance of Generative Adversarial Networks (GANs) by enforcing a consistency cost on the discriminator. We improve on this technique in several wa…
Small-GAN: Speeding Up GAN Training Using Core-sets
Samarth Sinha, Han Zhang, Anirudh Goyal +3
Recent work by Brock et al. (2018) suggests that Generative Adversarial Networks (GANs) benefit disproportionately from large mini-batch sizes. Unfortunately, using large batches i…
Improving Differentially Private Models with Active Learning
Zhengli Zhao, Nicolas Papernot, Sameer Singh +2
Broad adoption of machine learning techniques has increased privacy concerns for models trained on sensitive data such as medical records. Existing techniques for training differen…
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…
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…