35 citations · 37 across the 3 of their papers we have counts for
7 papers
RareGAN: Generating Samples for Rare Classes
Zinan Lin, Hao Liang, Giulia Fanti +1
We study the problem of learning generative adversarial networks (GANs) for a rare class of an unlabeled dataset subject to a labeling budget. This problem is motivated from practi…
Pareto GAN: Extending the Representational Power of GANs to Heavy-Tailed Distributions
Todd Huster, Jeremy E. J. Cohen, Zinan Lin +5
Generative adversarial networks (GANs) are often billed as "universal distribution learners", but precisely what distributions they can represent and learn is still an open questio…
MLGO: a Machine Learning Guided Compiler Optimizations Framework
Mircea Trofin, Yundi Qian, Eugene Brevdo +3
Leveraging machine-learning (ML) techniques for compiler optimizations has been widely studied and explored in academia. However, the adoption of ML in general-purpose, industry st…
Why Spectral Normalization Stabilizes GANs: Analysis and Improvements
Zinan Lin, Vyas Sekar, Giulia Fanti
Spectral normalization (SN) is a widely-used technique for improving the stability and sample quality of Generative Adversarial Networks (GANs). However, there is currently limited…
Using GANs for Sharing Networked Time Series Data: Challenges, Initial Promise, and Open Questions
Zinan Lin, Alankar Jain, Chen Wang +2
Limited data access is a longstanding barrier to data-driven research and development in the networked systems community. In this work, we explore if and how generative adversarial…
InfoGAN-CR and ModelCentrality: Self-supervised Model Training and Selection for Disentangling GANs
Zinan Lin, Kiran Koshy Thekumparampil, Giulia Fanti +1
Disentangled generative models map a latent code vector to a target space, while enforcing that a subset of the learned latent codes are interpretable and associated with distinct…