Contrastive Clustering

Contrastive clustering is an unsupervised machine learning technique aiming to group similar data points into clusters by learning representations that maximize similarity within clusters and dissimilarity between them. Current research focuses on improving the robustness and scalability of contrastive clustering methods, often employing deep neural networks, including autoencoders and Vision Transformers, and incorporating techniques like similarity-guided losses and multi-view architectures. This approach shows promise across diverse applications, from anomaly detection in graphs and DNA sequence analysis to image and video segmentation, and text clustering, demonstrating its potential to advance various fields by enabling efficient and accurate unsupervised data analysis.