Duet Robust Deep Subspace Clustering

Abstract

Subspace clustering has long been recognized as vulnerable toward gross corruptions -- the corruptions can easily mislead the estimation of the underlying subspace structure. Recently, deep extensions of traditional subspace clustering methods have shown their great power to boost the clustering performance. However, deep learning methods are, in themselves, more prone to be affected by data corruptions. This motivates us to design specific robust extensions for deep subspace clustering methods. More precisely, we contribute a new robust deep framework called Duet Robust Deep Subspace Clustering (DRDSC). Our main idea is to explicitly model the corrupted patterns from both the data reconstruction perspective and the latent self-expression perspective with two regularization norms. Moreover, since the two involved norms are non-smooth, we implement a smoothing technique for these norms to facilitate the back-propagation of our proposed network. Experiments carried out on read-world vision tasks with different noise settings demonstrate the effectiveness of our proposed method.