The concept of NC was first formally introduced by XLearning group at NeurIPS 2021 (Oral presentation), which highlighted the negative impacts of mismatched pairs on learning foundation models CLIP. Since then, NC has been studied in numerous tasks, e.g., fundamental models, retrieval, Re-ID, QA, dialogue systems, graph matching, video reasoning (in CVPR'21, NeurIPS'21, CVPR'22, TPAMI'22, AAAI'23, CVPR'23, ICLR'24, NeurIPS'24 etc.).

XLearning group proposed one of the first deep clustering methods (arxiv'15, IJCAI'16), which equips clustering algorithms with powerful discrimination ability to handle complex real-world data. In 2021, we proposed the contrastive clustering framework (AAAI'21, IJCV'22), which elegantly unifies representation learning and clustering by performing contrastive learning in the row and column spaces, respectively. Motivated by the classic k-means clustering algorithm, we designed the first interpretable unsupervised neural network, which is intrinsically explainable and transparent (JMLR'22). Recently, by looking back on the development of the clustering community, we wrote a survey that summarizes deep clustering methods from the prior perspective (Vicinagearth'24). Correspondingly, we propose a new externally guided clustering paradigm (ICML'24), seeking abundant yet regrettably overlooked external knowledge as priors to facilitate clustering.