DMS: Differentiable Mean Shift for Dataset Agnostic Task Specific
  Clustering Using Side Information
Michael A. Hobley Victor A. Prisacariu
We present a novel approach, in which we learn to cluster data directly from
side information, in the form of a small set of pairwise examples. Unlike
previous methods, with or without side information, we do not need to know the
number of clusters, their centers or any kind of distance metric for
similarity. Our method is able to divide the same data points in various ways
dependant on the needs of a specific task, defined by the side information.
Contrastingly, other work generally finds only the intrinsic, most obvious,
clusters. Inspired by the mean shift algorithm, we implement our new clustering
approach using a custom iterative neural network to create Differentiable Mean
Shift (DMS), a state of the art, dataset agnostic, clustering method. We found
that it was possible to train a strong cluster definition without enforcing a
constraint that each cluster must be presented during training. DMS outperforms
current methods in both the intrinsic and non-intrinsic dataset tasks.