  In this work, we investigate semi-supervised learning (SSL) for image
classification using adversarial training. Previous results have illustrated
that generative adversarial networks (GANs) can be used for multiple purposes.
Triple-GAN, which aims to jointly optimize model components by incorporating
three players, generates suitable image-label pairs to compensate for the lack
of labeled data in SSL with improved benchmark performance. Conversely, Bad (or
complementary) GAN, optimizes generation to produce complementary data-label
pairs and force a classifier's decision boundary to lie between data manifolds.
Although it generally outperforms Triple-GAN, Bad GAN is highly sensitive to
the amount of labeled data used for training. Unifying these two approaches, we
present unified-GAN (UGAN), a novel framework that enables a classifier to
simultaneously learn from both good and bad samples through adversarial
training. We perform extensive experiments on various datasets and demonstrate
that UGAN: 1) achieves state-of-the-art performance among other deep generative
models, and 2) is robust to variations in the amount of labeled data used for
training.
