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Conditional generative adversarial networks (cGANs) have gained aconsiderable attention in recent years due to its class-wise controllabilityand superior quality for complex generation tasks. We introduce a simple yeteffective approach to improving cGANs by measuring the discrepancy between thedata distribution and the model distribution on given samples. The proposedmeasure, coined the gap of log-densities (GOLD), provides an effectiveself-diagnosis for cGANs while being efficienty computed from thediscriminator. We propose three applications of the GOLD: example re-weighting,rejection sampling, and active learning, which improve the training, inference,and data selection of cGANs, respectively. Our experimental results demonstratethat the proposed methods outperform corresponding baselines for all threeapplications on different image datasets.

Mining GOLD Samples for Conditional GANs