Label Geometry Aware Discriminator for Conditional Generative Networks
- URL: http://arxiv.org/abs/2105.05501v1
- Date: Wed, 12 May 2021 08:17:25 GMT
- Title: Label Geometry Aware Discriminator for Conditional Generative Networks
- Authors: Suman Sapkota, Bidur Khanal, Binod Bhattarai, Bishesh Khanal, Tae-Kyun
Kim
- Abstract summary: Conditional Generative Adversarial Networks (GANs) can generate highly photo realistic images with desired target classes.
These synthetic images have not always been helpful to improve downstream supervised tasks such as image classification.
- Score: 40.89719383597279
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- 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.
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