290 citations · 1.3k across the 57 of their papers we have counts for
5 papers · 1 filter
Triple Generative Adversarial Networks
Chongxuan Li, Kun Xu, Jiashuo Liu +2
We propose a unified game-theoretical framework to perform classification and conditional image generation given limited supervision. It is formulated as a three-player minimax gam…
Understanding and Stabilizing GANs' Training Dynamics with Control Theory
Kun Xu, Chongxuan Li, Jun Zhu +1
Generative adversarial networks (GANs) are effective in generating realistic images but the training is often unstable. There are existing efforts that model the training dynamics…
Multi-objects Generation with Amortized Structural Regularization
Kun Xu, Chongxuan Li, Jun Zhu +1
Deep generative models (DGMs) have shown promise in image generation. However, most of the existing work learn the model by simply optimizing a divergence between the marginal dist…
Countering Noisy Labels By Learning From Auxiliary Clean Labels
Tsung Wei Tsai, Chongxuan Li, Jun Zhu
We consider the learning from noisy labels (NL) problem which emerges in many real-world applications. In addition to the widely-studied synthetic noise in the NL literature, we al…
To Relieve Your Headache of Training an MRF, Take AdVIL
Chongxuan Li, Chao Du, Kun Xu +3
We propose a black-box algorithm called {\it Adversarial Variational Inference and Learning} (AdVIL) to perform inference and learning on a general Markov random field (MRF). AdVIL…