  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.
