  Aligning distributions of view representations is a core component of today's
state of the art models for deep multi-view clustering. However, we identify
several drawbacks with na\"ively aligning representation distributions. We
demonstrate that these drawbacks both lead to less separable clusters in the
representation space, and inhibit the model's ability to prioritize views.
Based on these observations, we develop a simple baseline model for deep
multi-view clustering. Our baseline model avoids representation alignment
altogether, while performing similar to, or better than, the current state of
the art. We also expand our baseline model by adding a contrastive learning
component. This introduces a selective alignment procedure that preserves the
model's ability to prioritize views. Our experiments show that the contrastive
learning component enhances the baseline model, improving on the current state
of the art by a large margin on several datasets.
