3 papers
cs.LG2021
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…
cs.CV2021
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…
cs.LG2020
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…