Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: CLDA: Contrastive Learning for Semi-Supervised Domain Adaptation   Unsupervised Domain Adaptation (UDA) aims to align the labeled source
distribution with the unlabeled target distribution to obtain domain invariant
predictive models. However, the application of well-known UDA approaches does
not generalize well in Semi-Supervised Domain Adaptation (SSDA) scenarios where
few labeled samples from the target domain are available. In this paper, we
propose a simple Contrastive Learning framework for semi-supervised Domain
Adaptation (CLDA) that attempts to bridge the intra-domain gap between the
labeled and unlabeled target distributions and inter-domain gap between source
and unlabeled target distribution in SSDA. We suggest employing class-wise
contrastive learning to reduce the inter-domain gap and instance-level
contrastive alignment between the original (input image) and strongly augmented
unlabeled target images to minimize the intra-domain discrepancy. We have shown
empirically that both of these modules complement each other to achieve
superior performance. Experiments on three well-known domain adaptation
benchmark datasets namely DomainNet, Office-Home, and Office31 demonstrate the
effectiveness of our approach. CLDA achieves state-of-the-art results on all
the above datasets.

10
The categories of this paper are: q-bio
6
The categories of this paper are: astro-ph
7
The categories of this paper are: hep-th
5
The categories of this paper are: gr-qc
24
The categories of this paper are: dg-ga
18
The categories of this paper are: econ
22
The categories of this paper are: chao-dyn
15
The categories of this paper are: nucl-ex
16
The categories of this paper are: stat
35
The categories of this paper are: mtrl-th
37
The categories of this paper are: supr-con
13
The categories of this paper are: quant-ph
25
The categories of this paper are: patt-sol
11
The categories of this paper are: hep-lat
20
The categories of this paper are: acc-phys
28
The categories of this paper are: alg-geom