Label Geometry Aware Discriminator for Conditional Generative Networks

TL;DR

This paper introduces a novel discriminator loss for conditional GANs that leverages angular margin to generate higher fidelity images, improving their usefulness for downstream classification tasks.

Contribution

It proposes replacing the soft-max cross-entropy loss with an additive angular margin loss in the discriminator to enhance image quality and class separation in conditional GANs.

Findings

Outperforms state-of-the-art methods on RaFD and CIFAR-100 datasets.

Improves downstream classification accuracy using synthetic data.

Achieves better FID, GAN-train, and GAN-test scores.

Abstract

Multi-domain image-to-image translation with conditional Generative Adversarial Networks (GANs) can generate highly photo realistic images with desired target classes, yet these synthetic images have not always been helpful to improve downstream supervised tasks such as image classification. Improving downstream tasks with synthetic examples requires generating images with high fidelity to the unknown conditional distribution of the target class, which many labeled conditional GANs attempt to achieve by adding soft-max cross-entropy loss based auxiliary classifier in the discriminator. As recent studies suggest that the soft-max loss in Euclidean space of deep feature does not leverage their intrinsic angular distribution, we propose to replace this loss in auxiliary classifier with an additive angular margin (AAM) loss that takes benefit of the intrinsic angular distribution, and promotes intra-class compactness and inter-class separation to help generator synthesize high fidelity images. We validate our method on RaFD and CIFAR-100, two challenging face expression and natural image classification data set. Our method outperforms state-of-the-art methods in several different evaluation criteria including recently proposed GAN-train and GAN-test metrics designed to assess the impact of synthetic data on downstream classification task, assessing the usefulness in data augmentation for supervised tasks with prediction accuracy score and average confidence score, and the well known FID metric.