Search Results for author: Zhengli Zhao

Found 6 papers, 3 papers with code

Learnability and Complexity of Quantum Samples

1 code implementation22 Oct 2020 Murphy Yuezhen Niu, Andrew M. Dai, Li Li, Augustus Odena, Zhengli Zhao, Vadim Smelyanskyi, Hartmut Neven, Sergio Boixo

Given a quantum circuit, a quantum computer can sample the output distribution exponentially faster in the number of bits than classical computers.

Image Augmentations for GAN Training

no code implementations4 Jun 2020 Zhengli Zhao, Zizhao Zhang, Ting Chen, Sameer Singh, Han Zhang

We provide new state-of-the-art results for conditional generation on CIFAR-10 with both consistency loss and contrastive loss as additional regularizations.

Image Augmentation Image Generation

Top-k Training of GANs: Improving GAN Performance by Throwing Away Bad Samples

2 code implementations NeurIPS 2020 Samarth Sinha, Zhengli Zhao, Anirudh Goyal, Colin Raffel, Augustus Odena

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 computational cost: When updating the generator parameters, we simply zero out the gradient contributions from the elements of the batch that the critic scores as `least realistic'.

Improved Consistency Regularization for GANs

no code implementations11 Feb 2020 Zhengli Zhao, Sameer Singh, Honglak Lee, Zizhao Zhang, Augustus Odena, Han Zhang

Recent work has increased the performance of Generative Adversarial Networks (GANs) by enforcing a consistency cost on the discriminator.

Image Generation

Improving Differentially Private Models with Active Learning

no code implementations2 Oct 2019 Zhengli Zhao, Nicolas Papernot, Sameer Singh, Neoklis Polyzotis, Augustus Odena

Broad adoption of machine learning techniques has increased privacy concerns for models trained on sensitive data such as medical records.

Active Learning

Generating Natural Adversarial Examples

1 code implementation ICLR 2018 Zhengli Zhao, Dheeru Dua, Sameer Singh

Due to their complex nature, it is hard to characterize the ways in which machine learning models can misbehave or be exploited when deployed.

Adversarial Attack Image Classification +3

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