Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Improving out-of-distribution generalization via multi-task
  self-supervised pretraining   Self-supervised feature representations have been shown to be useful for
supervised classification, few-shot learning, and adversarial robustness. We
show that features obtained using self-supervised learning are comparable to,
or better than, supervised learning for domain generalization in computer
vision. We introduce a new self-supervised pretext task of predicting responses
to Gabor filter banks and demonstrate that multi-task learning of compatible
pretext tasks improves domain generalization performance as compared to
training individual tasks alone. Features learnt through self-supervision
obtain better generalization to unseen domains when compared to their
supervised counterpart when there is a larger domain shift between training and
test distributions and even show better localization ability for objects of
interest. Self-supervised feature representations can also be combined with
other domain generalization methods to further boost performance.

13
The categories of this paper are: quant-ph
32
The categories of this paper are: atom-ph
20
The categories of this paper are: acc-phys
35
The categories of this paper are: mtrl-th
23
The categories of this paper are: solv-int
24
The categories of this paper are: dg-ga
22
The categories of this paper are: chao-dyn
18
The categories of this paper are: econ
17
The categories of this paper are: q-fin
19
The categories of this paper are: eess
12
The categories of this paper are: nucl-th
0
The categories of this paper are: hep-ph
7
The categories of this paper are: hep-th
5
The categories of this paper are: gr-qc
26
The categories of this paper are: bayes-an
3
The categories of this paper are: physics