Deep incremental Optical Flow Coding For Learned Video Compression

Subspace clustering is widely used to find clusters in different subspaces within a dataset. Autoencoders are popular deep subspace clustering methods using feature extraction and dimensional reduction. However, neural networks are vulnerable to overfitting, and therefore have limited potential for unsupervised subspace clustering. This paper proposes a deep multi-view subspace clustering network with feature boosting module to successfully extract meaningful features in different views and to fuse multi-view representations in a complementary manner for enhanced clustering results. The multi-view boosting provides the robust features for unsupervised clustering by emphasizing the features and removing the redundant noise. Quantitative and qualitative analysis on various benchmark datasets verifies that the proposed method outperforms state-of-the-art subspace clustering methods.