Deep unsupervised learning with consistent inference of latent representations. (May 2018) - Chang, Jianlong; Wang, Lingfeng; Meng, Gaofeng; Xiang, Shiming; Pan, Chunhong
Highlights: An end-to-end unsupervised method is proposed to pre-train deep neural networks. The presented model is optimized under the EM algorithm framework. A series of variants of the proposed method are obtained. The convergence of our approach and its variants is theoretically verified. Abstract: Utilizing unlabeled data to train deep neural networks (DNNs) is a crucial but challenging task. In this paper, we propose an end-to-end approach to tackle this problem with consistent inference of latent representations. Specifically, each unlabeled data point is considered as a seed to generate a set of latent labeled data points by adding various random disturbances or transformations. Under the expectation maximization framework, DNNs can be trained in an unsupervised way by minimizing the distances between the data points with the same latent representations. Furthermore, several variants of our approach can be derived by applying regularized and sparse constraints during optimization. Theoretically, the convergence of the proposed method and its variants are fully analyzed. Experimental results show that the proposed approach can significantly improve the performance on various tasks, including image classification and clustering. Such results also indicate that our method can guide DNNs to learn more invariant feature representations in comparison with traditional unsupervised methods.