Self-supervised learning is a central component in recent approaches to deep multi-view clustering (MVC). However, we find large variations in the development of self-supervision-based methods for deep MVC, potentially slowing the progress of the field. To address this, we present DeepMVC, a unified framework for deep MVC that includes many recent methods as instances. We leverage our framework to make key observations about the effect of self-supervision, and in particular, drawbacks of aligning representations with contrastive learning. Further, we prove that contrastive alignment can negatively influence cluster separability, and that this effect becomes worse when the number of views increases. Motivated by our findings, we develop several new DeepMVC instances with new forms of self-supervision. We conduct extensive experiments and find that (i) in line with our theoretical findings, contrastive alignments decreases performance on datasets with many views; (ii) all methods benefit from some form of self-supervision; and (iii) our new instances outperform previous methods on several datasets. Based on our results, 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.

翻译：自监督学习是近年来深度多视角聚类（MVC）方法的核心组成部分。然而我们发现，基于自监督的深度MVC方法在发展中存在显著差异，这可能导致该领域发展速度放缓。为解决这一问题，我们提出DeepMVC——一个统一框架，该框架将众多近期方法作为其实例。我们利用该框架对自监督的影响进行关键性观察，尤其揭示了对比学习在表征对齐中的缺陷。进一步地，我们证明对比对齐会负面干扰聚类可分离性，且这种效应会随视角数量增加而加剧。受发现启发，我们开发了多个具备新型自监督形式的DeepMVC实例。通过大量实验发现：（i）与理论结果一致，对比对齐在多数视角数据集上的表现会下降；（ii）所有方法均能从某种形式的自监督中获益；（iii）我们的新实例在多个数据集上优于先前方法。基于实验结果，我们建议了未来研究的若干可行方向。为提升领域开放性，我们提供DeepMVC的开源实现，包含近期模型及新实例。该实现采用统一的评估协议，可促进对方法及其组件的公平、准确评估。