An Incremental Self-Labeling Strategy for Semi-Supervised Deep Learning Based on Generative Adversarial Networks

Abstract

The recent success of deep neural networks is attributed in part to large-scale well-labeled training data. However, with the ever-increasing size of modern datasets, combined with the difficulty of obtaining label information, semi-supervised learning (SSL) has become one of the most remarkable issues in data analysis. In this paper, we propose an Incremental Self-Labeling strategy for SSL based on Generative Adversarial Nets (ISL-GAN), which functions by constantly assigning unlabeled data with virtual labels for promoting the training process. Specifically, during the virtual labeling process, we introduce a temporal-based self-labeling strategy for safe and stable data labeling. Then, to dynamically assign more virtual labels to data during the training, we conduct a phased incremental label screening and updating strategy. Finally, to balance the contribution of samples with different loss during the training process, we further introduce the Balance factor Term (BT). The experimental results show that the proposed method gives rise to state-of-the-art semi-supervised learning results for the MNIST, CIFAR-10, and SVHN datasets. Particularly, our model performs well with fewer labeled conditions. With a dataset of only 1,000 labeled CIFAR-10 images with CONV-Large Net, a test error of 11.2% can be achieved, and nearly the same performance with a 3.5% test error can be achieved with both 500 and 1,000 image-labeled SVHN datasets.