SC2-Net: Self-supervised learning for multi-view complementarity representation and consistency fusion network

Multi-view clustering (MVC) seeks to improve the original single-view clustering by exploring the complementarity and consistency contained in multi-view data. While most subspace-based multi-view clustering methods now focus on exploring one of the consistency or complementarity features associated with multi-view datasets, rather than balancing the exploration of them. Meanwhile, the favored approach of combining deep learning tends to design the network structure to be relatively complex and then superimpose the constraints of multiple loss functions. Additionally, training results with unlabeled datasets are often unsatisfactory. To solve the aforementioned concerns, we present an innovative deep convolutional clustering network (SC2-Net) backed by self-supervised learning. SC2-Net learns multi-view complementarity representation and consistency fusion between views which adheres to the two principles of MVC. The clustering labels will be obtained by cooperating with k-means, the overall structure is simple but efficient. In addition, we supervise the network training by using two self-supervised loss functions, making the training process free from using data with annotations. We test the proposed network under multiple sets of experimental parameter combinations and prove its effectiveness and robustness.