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Conditional generative adversarial networks (cGANs) have demonstratedremarkable success due to their class-wise controllability and superior qualityfor complex generation tasks. Typical cGANs solve the joint distributionmatching problem by decomposing two easier sub-problems: marginal matching andconditional matching. From our toy experiments, we found that it is the best toapply only conditional matching to certain samples due to the content-awareoptimization of the discriminator. This paper proposes a simple (a few lines ofcode) but effective training methodology, selective focusing learning, whichenforces the discriminator and generator to learn easy samples of each classrapidly while maintaining diversity. Our key idea is to selectively applyconditional and joint matching for the data in each mini-batch. We conductedexperiments on recent cGAN variants in ImageNet (64x64 and 128x128), CIFAR-10,and CIFAR-100 datasets, and improved the performance significantly (up to35.18% in terms of FID) without sacrificing diversity.

Selective Focusing Learning in Conditional GANs