Transductive Semi-Supervised Deep Learning Using Min-Max Features

Abstract

In this paper, we propose Transductive Semi-Supervised Deep Learning (TSSDL) method that is effective for training Deep Convolutional Neural Network (DCNN) models. The method applies transductive learning principle to DCNN training, introduces confidence levels on unlabeled image samples to overcome unreliable label estimates on outliers and uncertain samples, and develops the Min-Max Feature (MMF) regularization that encourages DCNN to learn feature descriptors with better between-class separability and within-class compactness. TSSDL method is independent of any DCNN architectures and complementary to the latest Semi-Supervised Learning (SSL) methods. Comprehensive experiments on the benchmark datasets CIFAR10 and SVHN have shown that the DCNN model trained by the proposed TSSDL method can produce image classification accuracies compatible to the state-of-the-art SSL methods, and that combining TSSDL with the Mean Teacher method can produce the best classification accuracies on the two benchmark datasets.