Self-Supervised Deep Subspace Clustering with Entropy-norm
Auto-Encoder based deep subspace clustering (DSC) is widely used in computer vision, motion segmentation and image processing. However, it suffers from the following three issues in the self-expressive matrix learning process: the first one is less useful information for learning self-expressive weights due to the simple reconstruction loss; the second one is that the construction of the self-expression layer associated with the sample size requires high-computational cost; and the last one is the limited connectivity of the existing regularization terms. In order to address these issues, in this paper we propose a novel model named Self-Supervised deep Subspace Clustering with Entropy-norm (S$^{3}$CE). Specifically, S$^{3}$CE exploits a self-supervised contrastive network to gain a more effetive feature vector. The local structure and dense connectivity of the original data benefit from the self-expressive layer and additional entropy-norm constraint. Moreover, a new module with data enhancement is designed to help S$^{3}$CE focus on the key information of data, and improve the clustering performance of positive and negative instances through spectral clustering. Extensive experimental results demonstrate the superior performance of S$^{3}$CE in comparison to the state-of-the-art approaches.
Code (0)
등록된 구현이 없습니다.
Tasks
ClusteringMotion SegmentationSimilar Papers 제목 키워드 기반
Maximum Entropy Subspace Clustering Network
Deep subspace clustering networks have attracted much attention in subspace clustering, in which an auto-encoder non-linearly maps the input data into a latent space, and a fully connected layer named self-expressiveness…
ClusteringRobust Self-Supervised Convolutional Neural Network for Subspace Clustering and Classification
Insufficient capability of existing subspace clustering methods to handle data coming from nonlinear manifolds, data corruptions, and out-of-sample data hinders their applicability to address real-world clustering and cl…
ClusteringGeneral ClassificationSparse Subspace Clustering with Entropy-Norm
Sparse subspace clustering (SSC) and spectral clustering (SC) are both state-of-the-art methods to identify complex clusters in high-dimensional input space. However, there are few researches to discuss the relation betw…
ClusteringSelf-Supervised Convolutional Subspace Clustering Network
Subspace clustering methods based on data self-expression have become very popular for learning from data that lie in a union of low-dimensional linear subspaces. However, the applicability of subspace clustering has bee…
ClusteringImage ClusteringProvable Subspace Clustering: When LRR meets SSC
Sparse Subspace Clustering (SSC) and Low-Rank Representation (LRR) are both considered as the state-of-the-art methods for {\em subspace clustering}. The two methods are fundamentally similar in that both are convex opti…
Clustering