Semi-Supervised Learning with Coevolutionary Generative Adversarial Networks

Abstract

It can be expensive to label images for classification. Good classifiers or high-quality images can be trained on unlabeled data with Generative Adversarial Network~(GAN) methods. We use coevolutionary algorithms with Semi-Supervised GANs (SSL-GANs) that work with a few labeled and some more unlabeled images to train both a good classifier and a high-quality image generator. A spatial coevolutionary algorithm introduces diversity into the GAN training. We use a two-dimensional grid of GANs to gain discriminator loss diversity with a distributed cell-level coevolutionary algorithm. The GAN components are exchanged between neighboring cells based on performance and population-based hyperparameter tuning. The approach is demonstrated on two separate benchmark datasets, and with only a few labels, we simultaneously achieve good classification accuracy and high generated image quality score. In addition, the generated image quality and classification accuracy are competitive to state-of-the-art methods.