Online Deep Clustering for Unsupervised Representation Learning

Xiaohang Zhan, Jiahao Xie, Ziwei Liu, Yew-Soon Ong, Chen Change Loy; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020, pp. 6688-6697

Abstract


Joint clustering and feature learning methods have shown remarkable performance in unsupervised representation learning. However, the training schedule alternating between feature clustering and network parameters update leads to unstable learning of visual representations. To overcome this challenge, we propose Online Deep Clustering (ODC) that performs clustering and network update simultaneously rather than alternatingly. Our key insight is that the cluster centroids should evolve steadily in keeping the classifier stably updated. Specifically, we design and maintain two dynamic memory modules, i.e., samples memory to store samples' labels and features, and centroids memory for centroids evolution. We break down the abrupt global clustering into steady memory update and batch-wise label re-assignment. The process is integrated into network update iterations. In this way, labels and the network evolve shoulder-to-shoulder rather than alternatingly. Extensive experiments demonstrate that ODC stabilizes the training process and boosts the performance effectively.

Related Material


[pdf] [arXiv]
[bibtex]
@InProceedings{Zhan_2020_CVPR,
author = {Zhan, Xiaohang and Xie, Jiahao and Liu, Ziwei and Ong, Yew-Soon and Loy, Chen Change},
title = {Online Deep Clustering for Unsupervised Representation Learning},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2020}
}