Title: On the Role of Self-supervision in Deep Multi-view Clustering. Abstract: Self-supervised learning is a central component in many recent approaches to deep multi-view clustering (MVC). However, we find large variations in the motivation and design of self-supervision-based methods for deep MVC. To address this, we present DeepMVC, a new, unified framework for deep MVC. Crucially, we show that many recent methods can be regarded as instances of our framework -- allowing us to implement recent methods in a unified and consistent manner. We make key observations about the effect of self-supervision, and in particular, drawbacks of representation alignment. Motivated by these insights, we develop several new DeepMVC instances, with new forms of self-supervision. We conduct extensive experiments, and find that (i) the popular contrastive alignment degrades performance when the number of views becomes large; (ii) all methods benefit from some form of self-supervision; and (iii) our new instances outperform previous methods on several datasets. Based on our findings, we suggest several promising directions for future research. To enhance the openness of the field, we provide an open-source implementation of DeepMVC, including recent models and our new instances. Our implementation includes a consistent evaluation protocol, facilitating fair and accurate evaluation of methods and components.
