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Multi-domain image-to-image translation with conditional GenerativeAdversarial Networks (GANs) can generate highly photo realistic images withdesired target classes, yet these synthetic images have not always been helpfulto improve downstream supervised tasks such as image classification. Improvingdownstream tasks with synthetic examples requires generating images with highfidelity to the unknown conditional distribution of the target class, whichmany labeled conditional GANs attempt to achieve by adding soft-maxcross-entropy loss based auxiliary classifier in the discriminator. As recentstudies suggest that the soft-max loss in Euclidean space of deep feature doesnot leverage their intrinsic angular distribution, we propose to replace thisloss in auxiliary classifier with an additive angular margin (AAM) loss thattakes benefit of the intrinsic angular distribution, and promotes intra-classcompactness and inter-class separation to help generator synthesize highfidelity images.  We validate our method on RaFD and CIFAR-100, two challenging face expressionand natural image classification data set. Our method outperformsstate-of-the-art methods in several different evaluation criteria includingrecently proposed GAN-train and GAN-test metrics designed to assess the impactof synthetic data on downstream classification task, assessing the usefulnessin data augmentation for supervised tasks with prediction accuracy score andaverage confidence score, and the well known FID metric.

Label Geometry Aware Discriminator for Conditional Generative Networks