Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Estimating Galactic Distances From Images Using Self-supervised
  Representation Learning   We use a contrastive self-supervised learning framework to estimate distances
to galaxies from their photometric images. We incorporate data augmentations
from computer vision as well as an application-specific augmentation accounting
for galactic dust. We find that the resulting visual representations of galaxy
images are semantically useful and allow for fast similarity searches, and can
be successfully fine-tuned for the task of redshift estimation. We show that
(1) pretraining on a large corpus of unlabeled data followed by fine-tuning on
some labels can attain the accuracy of a fully-supervised model which requires
2-4x more labeled data, and (2) that by fine-tuning our self-supervised
representations using all available data labels in the Main Galaxy Sample of
the Sloan Digital Sky Survey (SDSS), we outperform the state-of-the-art
supervised learning method.

13
The categories of this paper are: quant-ph
15
The categories of this paper are: nucl-ex
35
The categories of this paper are: mtrl-th
32
The categories of this paper are: atom-ph
25
The categories of this paper are: patt-sol
37
The categories of this paper are: supr-con
17
The categories of this paper are: q-fin
16
The categories of this paper are: stat
31
The categories of this paper are: ao-sci
9
The categories of this paper are: nlin
33
The categories of this paper are: plasm-ph
21
The categories of this paper are: adap-org
30
The categories of this paper are: q-alg
0
The categories of this paper are: hep-ph
28
The categories of this paper are: alg-geom
11
The categories of this paper are: hep-lat