LAUSR

Abstract Clustering is a difficult but crucial task in pattern recognition and machine learning. Inherently, clustering methods are always subject to the uncertainty of similarities between samples. To weaken the impact of such uncertainty, we develop Deep Clustering with both Adaptive Siamese Loss (ASL) and ReConstruction Loss (RCL) to adaptively consider the similarities and stabilize the clustering process. Technically, ASL is focus on mapping the samples from the data space to the same representations or the orthogonal representations, and RCL provides a priori knowledge to stabilize the clustering process. Benefiting from such artful modelling, DCSR is in a position to endow deep networks to stably learn one-hot representations, yielding an end-to-end mechanism for clustering and representation learning. Extensive experiments demonstrate that our model achieves state-of-the-art performance on popular datasets, including image, audio, and text.