BYOL Network Based Contrastive Clustering

This passage introduces a new clustering approach called BYOL network-based Contrastive Clustering (BCC). This methodology builds on the BYOL framework, which consists of two co-optimized networks: the online and target networks. The online network aims to predict the outputs of the target network while maintaining the similarity relationship between views. The target network is stop-gradient and only updated by EMA of the online network. Additionally, the study incorporates the concept of adversarial learning into the approach to further refine the cluster assignments. The effectiveness of BCC is demonstrated on several mainstream image datasets, achieving impressive results without the need for negative samples or a large batch size. This research showcases the feasibility of using the BYOL architecture for clustering and proposes a novel clustering method that eliminates the problems bring by negative samples and reduce the computational complexity.