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. Code: https://github.com/open-mmlab/OpenSelfSup.

本文提出了一种在线深度聚类方法(ODC)，将聚类和网络更新同时进行，而不是交替进行，通过设计和维护两个动态存储模块(样本存储和中心存储)来使聚类中心稳定演化以保证分类器的稳定更新，实现了稳定的无监督表征学习。实验证明ODC有效地稳定了训练过程并提高了性能。

在线深度聚类用于无监督表示学习