  Semi-supervised learning has been gaining attention as it allows for
performing image analysis tasks such as classification with limited labeled
data. Some popular algorithms using Generative Adversarial Networks (GANs) for
semi-supervised classification share a single architecture for classification
and discrimination. However, this may require a model to converge to a separate
data distribution for each task, which may reduce overall performance. While
progress in semi-supervised learning has been made, less addressed are
small-scale, fully-supervised tasks where even unlabeled data is unavailable
and unattainable. We therefore, propose a novel GAN model namely External
Classifier GAN (EC-GAN), that utilizes GANs and semi-supervised algorithms to
improve classification in fully-supervised regimes. Our method leverages a GAN
to generate artificial data used to supplement supervised classification. More
specifically, we attach an external classifier, hence the name EC-GAN, to the
GAN's generator, as opposed to sharing an architecture with the discriminator.
Our experiments demonstrate that EC-GAN's performance is comparable to the
shared architecture method, far superior to the standard data augmentation and
regularization-based approach, and effective on a small, realistic dataset.
