Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Constrained Mean Shift Using Distant Yet Related Neighbors for
  Representation Learning   We are interested in representation learning in self-supervised, supervised,
or semi-supervised settings. The prior work on applying mean-shift idea for
self-supervised learning, MSF, generalizes the BYOL idea by pulling a query
image to not only be closer to its other augmentation, but also to the nearest
neighbors (NNs) of its other augmentation. We believe the learning can benefit
from choosing far away neighbors that are still semantically related to the
query. Hence, we propose to generalize MSF algorithm by constraining the search
space for nearest neighbors. We show that our method outperforms MSF in SSL
setting when the constraint utilizes a different augmentation of an image, and
outperforms PAWS in semi-supervised setting with less training resources when
the constraint ensures the NNs have the same pseudo-label as the query.

26
The categories of this paper are: bayes-an
30
The categories of this paper are: q-alg
11
The categories of this paper are: hep-lat
35
The categories of this paper are: mtrl-th
22
The categories of this paper are: chao-dyn
0
The categories of this paper are: hep-ph
37
The categories of this paper are: supr-con
5
The categories of this paper are: gr-qc
4
The categories of this paper are: cond-mat
9
The categories of this paper are: nlin
25
The categories of this paper are: patt-sol
36
The categories of this paper are: cmp-lg
14
The categories of this paper are: math-ph
24
The categories of this paper are: dg-ga
6
The categories of this paper are: astro-ph
19
The categories of this paper are: eess