Deep Co-Training for Semi-Supervised Image Recognition

Siyuan Qiao, Wei Shen, Zhishuai Zhang, Bo Wang, Alan Yuille; Proceedings of the European Conference on Computer Vision (ECCV), 2018, pp. 135-152


In this paper, we study the problem of semi-supervised image recognition, which is to learn classifiers using both labeled and unlabeled images. We present Deep Co-Training, a deep learning based method inspired by the Co-Training framework. The original Co-Training learns two classifiers on two views which are data from different sources that describe the same instances. To extend this concept to deep learning, Deep Co-Training trains multiple deep neural networks to be the different views and exploits adversarial examples to encourage view difference, in order to prevent the networks from collapsing into each other. As a result, the co-trained networks provide different and complementary information about the data, which is necessary for the Co-Training framework to achieve good results. We test our method on SVHN, CIFAR-10/100 and ImageNet datasets, and our method outperforms the previous state-of-the-art methods by a large margin.

Related Material

[pdf] [arXiv]
author = {Qiao, Siyuan and Shen, Wei and Zhang, Zhishuai and Wang, Bo and Yuille, Alan},
title = {Deep Co-Training for Semi-Supervised Image Recognition},
booktitle = {Proceedings of the European Conference on Computer Vision (ECCV)},
month = {September},
year = {2018}