Deep Semi-Supervised Learning

Abstract

Convolutional neural networks (CNNs) attain state-of-the-art performance on various classification tasks assuming a sufficiently large number of labeled training examples. Unfortunately, curating sufficiently large labeled training dataset requires human involvement, which is expensive and time consuming. Semi-supervised methods can alleviate this problem by utilizing a limited number of labeled data in conjunction with sufficiently large unlabeled data to construct a classification model. Self-training techniques are among the earliest semi-supervised methods proposed to enhance learning by utilizing unlabeled data. In this paper, we propose a deep semi-supervised learning (DSSL) self-training method that utilizes the strengths of both supervised and unsupervised learning within a single model. We measure the efficacy of the proposed method on semi-supervised visual object classification tasks using the datasets CIFAR-10, CIFAR-100, STL-10, MNIST, and SVHN. The experiments show that DSSL surpasses semi-supervised state-of-the-art methods for most of the aforementioned datasets.