Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Towards Domain-Agnostic Contrastive Learning   Despite recent success, most contrastive self-supervised learning methods are
domain-specific, relying heavily on data augmentation techniques that require
knowledge about a particular domain, such as image cropping and rotation. To
overcome such limitation, we propose a novel domain-agnostic approach to
contrastive learning, named DACL, that is applicable to domains where
invariances, and thus, data augmentation techniques, are not readily available.
Key to our approach is the use of Mixup noise to create similar and dissimilar
examples by mixing data samples differently either at the input or hidden-state
levels. To demonstrate the effectiveness of DACL, we conduct experiments across
various domains such as tabular data, images, and graphs. Our results show that
DACL not only outperforms other domain-agnostic noising methods, such as
Gaussian-noise, but also combines well with domain-specific methods, such as
SimCLR, to improve self-supervised visual representation learning. Finally, we
theoretically analyze our method and show advantages over the Gaussian-noise
based contrastive learning approach.

37
The categories of this paper are: supr-con
22
The categories of this paper are: chao-dyn
25
The categories of this paper are: patt-sol
4
The categories of this paper are: cond-mat
10
The categories of this paper are: q-bio
5
The categories of this paper are: gr-qc
18
The categories of this paper are: econ
27
The categories of this paper are: comp-gas
7
The categories of this paper are: hep-th
23
The categories of this paper are: solv-int
11
The categories of this paper are: hep-lat
0
The categories of this paper are: hep-ph
31
The categories of this paper are: ao-sci
3
The categories of this paper are: physics
29
The categories of this paper are: funct-an
28
The categories of this paper are: alg-geom