StyleGAN2 with Adaptive Discriminator Augmentation (ADA). One of the long standing challenges with Generative Adversarial Networks (GANs) has been to train it with little data.
One of the long standing challenges with Generative Adversarial Networks (GANs) has been to train it with little data. The key problem with small datasets is that the discriminator quickly overfits to the training examples. The discriminator’s job is to classify its inputs as either fake or real, but due to overfitting, it rejects everything other than the training dataset as fake. As a result, the generator receives very little feedback to improve its generations and the training collapses. In this article we discuss a recent work by Karras et al. _[R1] that tackles this problem via **_Adaptive Discriminator Augmentation**.
Fig 1. GAN Training Objective — match generated image distribution x and real image distribution y. Left: x != y, Right: x = y
In almost all areas of deep learning, data augmentation is the standard solution against overfitting. For example, training image classifiers under rotation, noise, blur, etc. leads to increasing invariance to these semantics-preserving distortions — a highly desirable quality in a classifier. However, this doesn’t work directly for training GANs, as the generator would learn to produce the augmented distribution. This “leaking” of augmentations to the generated samples is highly undesirable.
The authors propose an augmentation technique called _stochastic discriminator augmentation _to overcome this “leaking” issues. They evaluate the discriminator only using augmented images, and do this also when training the generator. Discriminator augmentations corresponds to putting distorting goggles on the discriminator, and asking the generator to produce samples that cannot be distinguished from the training set when viewed through the goggles.
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