Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: VisDA-2021 Competition Universal Domain Adaptation to Improve
  Performance on Out-of-Distribution Data   Progress in machine learning is typically measured by training and testing a
model on the same distribution of data, i.e., the same domain. This
over-estimates future accuracy on out-of-distribution data. The Visual Domain
Adaptation (VisDA) 2021 competition tests models' ability to adapt to novel
test distributions and handle distributional shift. We set up unsupervised
domain adaptation challenges for image classifiers and will evaluate adaptation
to novel viewpoints, backgrounds, modalities and degradation in quality. Our
challenge draws on large-scale publicly available datasets but constructs the
evaluation across domains, rather that the traditional in-domain bench-marking.
Furthermore, we focus on the difficult "universal" setting where, in addition
to input distribution drift, methods may encounter missing and/or novel classes
in the target dataset. Performance will be measured using a rigorous protocol,
comparing to state-of-the-art domain adaptation methods with the help of
established metrics. We believe that the competition will encourage further
improvement in machine learning methods' ability to handle realistic data in
many deployment scenarios.

11
The categories of this paper are: hep-lat
23
The categories of this paper are: solv-int
20
The categories of this paper are: acc-phys
6
The categories of this paper are: astro-ph
36
The categories of this paper are: cmp-lg
30
The categories of this paper are: q-alg
4
The categories of this paper are: cond-mat
10
The categories of this paper are: q-bio
7
The categories of this paper are: hep-th
35
The categories of this paper are: mtrl-th
34
The categories of this paper are: chem-ph
21
The categories of this paper are: adap-org
9
The categories of this paper are: nlin
2
The categories of this paper are: math
0
The categories of this paper are: hep-ph
26
The categories of this paper are: bayes-an