Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Detecting Bias in Transfer Learning Approaches for Text Classification   Classification is an essential and fundamental task in machine learning,
playing a cardinal role in the field of natural language processing (NLP) and
computer vision (CV). In a supervised learning setting, labels are always
needed for the classification task. Especially for deep neural models, a large
amount of high-quality labeled data are required for training. However, when a
new domain comes out, it is usually hard or expensive to acquire the labels.
Transfer learning could be an option to transfer the knowledge from a source
domain to a target domain. A challenge is that these two domains can be
different, either on the feature distribution, or the class distribution for
the nature of the samples. In this work, we evaluate some existing transfer
learning approaches on detecting the bias of imbalanced classes including
traditional and deep models. Besides, we propose an approach to bridge the gap
of the domain class imbalance issue.

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The categories of this paper are: hep-th
11
The categories of this paper are: hep-lat
16
The categories of this paper are: stat
33
The categories of this paper are: plasm-ph
21
The categories of this paper are: adap-org
24
The categories of this paper are: dg-ga
35
The categories of this paper are: mtrl-th
17
The categories of this paper are: q-fin
23
The categories of this paper are: solv-int
10
The categories of this paper are: q-bio
36
The categories of this paper are: cmp-lg
34
The categories of this paper are: chem-ph
30
The categories of this paper are: q-alg
32
The categories of this paper are: atom-ph
18
The categories of this paper are: econ
31
The categories of this paper are: ao-sci