Guangchang Deng — accepted AI-conference papers

2020

Regularizing Discriminative Capability of CGANs for Semi-Supervised Generative Learning

Yi Liu, Guangchang Deng, Xiangping Zeng, Si Wu, Zhiwen Yu, Hau-San Wong

CVPR 2020poster

Semi-supervised generative learning aims to learn the underlying class-conditional distribution of partially labeled data. Generative Adversarial Networks (GANs) have led to promising progress in this task. However, it still needs to further explore the issue of imbalance between real labeled data a…

Cited by 29 PDF ↗Scholar ↗

2019

Enhancing TripleGAN for Semi-Supervised Conditional Instance Synthesis and Classification

Si Wu, Guangchang Deng, Jichang Li, Rui Li, Zhiwen Yu, Hau-San Wong

CVPR 2019poster

Learning class-conditional data distributions is crucial for Generative Adversarial Networks (GAN) in semi-supervised learning. To improve both instance synthesis and classification in this setting, we propose an enhanced TripleGAN (EnhancedTGAN) model in this work. We follow the adversarial trainin…

Cited by 41 PDF ↗Scholar ↗