Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Domain Generalisation with Domain Augmented Supervised Contrastive
  Learning (Student Abstract)   Domain generalisation (DG) methods address the problem of domain shift, when
there is a mismatch between the distributions of training and target domains.
Data augmentation approaches have emerged as a promising alternative for DG.
However, data augmentation alone is not sufficient to achieve lower
generalisation errors. This project proposes a new method that combines data
augmentation and domain distance minimisation to address the problems
associated with data augmentation and provide a guarantee on the learning
performance, under an existing framework. Empirically, our method outperforms
baseline results on DG benchmarks.

26
The categories of this paper are: bayes-an
32
The categories of this paper are: atom-ph
37
The categories of this paper are: supr-con
11
The categories of this paper are: hep-lat
4
The categories of this paper are: cond-mat
36
The categories of this paper are: cmp-lg
31
The categories of this paper are: ao-sci
22
The categories of this paper are: chao-dyn
12
The categories of this paper are: nucl-th
35
The categories of this paper are: mtrl-th
34
The categories of this paper are: chem-ph
33
The categories of this paper are: plasm-ph
9
The categories of this paper are: nlin
17
The categories of this paper are: q-fin
8
The categories of this paper are: hep-ex
10
The categories of this paper are: q-bio