  Existing deep clustering methods rely on either contrastive or
non-contrastive representation learning for downstream clustering task.
Contrastive-based methods thanks to negative pairs learn uniform
representations for clustering, in which negative pairs, however, may
inevitably lead to the class collision issue and consequently compromise the
clustering performance. Non-contrastive-based methods, on the other hand, avoid
class collision issue, but the resulting non-uniform representations may cause
the collapse of clustering. To enjoy the strengths of both worlds, this paper
presents a novel end-to-end deep clustering method with prototype scattering
and positive sampling, termed ProPos. Specifically, we first maximize the
distance between prototypical representations, named prototype scattering loss,
which improves the uniformity of representations. Second, we align one
augmented view of instance with the sampled neighbors of another view --
assumed to be truly positive pair in the embedding space -- to improve the
within-cluster compactness, termed positive sampling alignment. The strengths
of ProPos are avoidable class collision issue, uniform representations,
well-separated clusters, and within-cluster compactness. By optimizing ProPos
in an end-to-end expectation-maximization framework, extensive experimental
results demonstrate that ProPos achieves competing performance on
moderate-scale clustering benchmark datasets and establishes new
state-of-the-art performance on large-scale datasets. Source code is available
at \url{https://github.com/Hzzone/ProPos}.
