37 citations · 37 across the 3 of their papers we have counts for
4 papers
Improving Model Compatibility of Generative Adversarial Networks by Boundary Calibration
Si-An Chen, Chun-Liang Li, Hsuan-Tien Lin
Generative Adversarial Networks (GANs) is a powerful family of models that learn an underlying distribution to generate synthetic data. Many existing studies of GANs focus on impro…
A Unified View of cGANs with and without Classifiers
Si-An Chen, Chun-Liang Li, Hsuan-Tien Lin
Conditional Generative Adversarial Networks (cGANs) are implicit generative models which allow to sample from class-conditional distributions. Existing cGANs are based on a wide ra…
Adversarial Unlearning of Backdoors via Implicit Hypergradient
Yi Zeng, Si Chen, Won Park +3
We propose a minimax formulation for removing backdoors from a given poisoned model based on a small set of clean data. This formulation encompasses much of prior work on backdoor…
Knowledge-Enriched Distributional Model Inversion Attacks
Si Chen, Mostafa Kahla, Ruoxi Jia +1
Model inversion (MI) attacks are aimed at reconstructing training data from model parameters. Such attacks have triggered increasing concerns about privacy, especially given a grow…