Robust Conditional GAN from Uncertainty-Aware Pairwise Comparisons


  • Ligong Han Rutgers University
  • Ruijiang Gao The University of Texas at Austin
  • Mun Kim Rutgers University
  • Xin Tao Tencent YouTu Lab
  • Bo Liu JD Finance America Corporation
  • Dimitris Metaxas Rutgers University



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 efficiently 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*.




How to Cite

Han, L., Gao, R., Kim, M., Tao, X., Liu, B., & Metaxas, D. (2020). Robust Conditional GAN from Uncertainty-Aware Pairwise Comparisons. Proceedings of the AAAI Conference on Artificial Intelligence, 34(07), 10909-10916.



AAAI Technical Track: Vision