Exploring Non-Contrastive Representation Learning for Deep Clustering

• URL: http://arxiv.org/abs/2111.11821v1
• Date: Tue, 23 Nov 2021 12:21:53 GMT
• Title: Exploring Non-Contrastive Representation Learning for Deep Clustering
• Authors: Zhizhong Huang, Jie Chen, Junping Zhang, Hongming Shan
• Abstract summary: Non-contrastive representation learning for deep clustering, termed NCC, is based on BYOL, a representative method without negative examples. NCC forms an embedding space where all clusters are well-separated and within-cluster examples are compact. Experimental results on several clustering benchmark datasets including ImageNet-1K demonstrate that NCC outperforms the state-of-the-art methods by a significant margin.
• Score: 23.546602131801205
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Existing deep clustering methods rely on contrastive learning for representation learning, which requires negative examples to form an embedding space where all instances are well-separated. However, the negative examples inevitably give rise to the class collision issue, compromising the representation learning for clustering. In this paper, we explore non-contrastive representation learning for deep clustering, termed NCC, which is based on BYOL, a representative method without negative examples. First, we propose to align one augmented view of instance with the neighbors of another view in the embedding space, called positive sampling strategy, which avoids the class collision issue caused by the negative examples and hence improves the within-cluster compactness. Second, we propose to encourage alignment between two augmented views of one prototype and uniformity among all prototypes, named prototypical contrastive loss or ProtoCL, which can maximize the inter-cluster distance. Moreover, we formulate NCC in an Expectation-Maximization (EM) framework, in which E-step utilizes spherical k-means to estimate the pseudo-labels of instances and distribution of prototypes from a target network and M-step leverages the proposed losses to optimize an online network. As a result, NCC forms an embedding space where all clusters are well-separated and within-cluster examples are compact. Experimental results on several clustering benchmark datasets including ImageNet-1K demonstrate that NCC outperforms the state-of-the-art methods by a significant margin.

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