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.

翻译：由于最近的研究表明,Euclidean深层地貌空间的软成像损失并不能利用其内在的角分布,因此我们提议,用一个具有内在角分布效益的添加式角边缘(AAM)损失来取代辅助分类中的这一损失,并提倡本级内部缩压和跨级分离,以帮助发电机合成高精确度图像。 我们验证了我们在歧视者体内添加软最大跨热带流失辅助分类法和CIRA-100的方法,这是两种具有挑战性的面部表达和自然图像分类数据集。 我们的方法在几个不同的评价标准中,包括最近提出的GAN-评级和对预测前阶段的预测评估,以及最近提出的对GAN-AAN-监督性数据评级和对GAAN-ADR的预测性平均评估,以及最近提出的对GAN-ANS-CAR-100的预测性能分级评估。