Unsupervised Embedding Learning With Mutual-Information Graph Convolutional Networks

Abstract

Recently, methods for unsupervised embedding learning have exhibited promising results for extracting desirable representations from unlabeled samples. In general, most methods learn the feature embeddings by handling each sample individually while the structural and semantic relationships between samples are not fully exploited. As a result, the learned embeddings are not sufficiently discriminative. To make use of such inter-sample information for deep embedding learning, this paper proposes an unsupervised method based on the graph convolutional network (GCN). On one hand, our method encodes structural information between the samples corresponding to the nodes in a local neighbourhood of the GCN graph. On the other hand, it leverages the mutual information between the original samples and the augmented ones to ensure that they are globally consistent with each other. Extensive experiments show that our method is not just robust to augmentation perturbations, but also learns discriminative embeddings. Consequently, it achieves the state-of-the-art performance on several challenging datasets.