Robust Conditional GAN from Uncertainty-Aware Pairwise Comparisons

Conditional generative adversarial networks have shown exceptional generation performance over the past few years. However, they require large numbers of annotations. To address this problem, we propose a novel generative adversarial network utilizing weak supervision in the form of pairwise comparisons (PC-GAN) for image attribute editing. In the light of Bayesian uncertainty estimation and noise-tolerant adversarial training, PC-GAN can estimate attribute rating ef- ficiently and demonstrate robust performance in noise resistance. Through extensive experiments, we show both qualitatively and quantitatively that PC-GAN performs comparably with fully-supervised methods and outperforms unsupervised baselines. Code and Supplementary can be found on the project website∗.

Authors

• Ligong Han
• Ruijiang Gao University of Texas at Dallas
• Mun Kim
• Xin Tao
• Bo Liu Peking University
• Dimitris Metaxas

Keywords

No keywords are indexed for this paper.