# Detecting Bias in Transfer Learning Approaches for Text Classification

arXiv: https://arxiv.org/abs/2102.02114 · HTML: https://mlsanity.com/paper/2102.02114/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 16 references that are in the map.
- First posted on arXiv 3 Feb 2021.

## Builds on (most cited first, 6 of 6)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [Learning Transferable Features with Deep Adaptation Networks](https://mlsanity.com/paper/1502.02791/) (2015)
- [Adversarial Discriminative Domain Adaptation](https://mlsanity.com/paper/1702.05464/) (2017)
- [NIPS 2016 Tutorial: Generative Adversarial Networks](https://mlsanity.com/paper/1701.00160/) (2016)
- [A Sensitivity Analysis of (and Practitioners' Guide to) Convolutional Neural Networks for Sentence Classification](https://mlsanity.com/paper/1510.03820/) (2015)

## Built on by (most cited first, 2 of 2)

- [Efficient Variational Graph Autoencoders for Unsupervised Cross-domain Prerequisite Chains](https://mlsanity.com/paper/2109.08722/) (2021)
- [Federated and Transfer Learning for Cancer Detection Based on Image Analysis](https://mlsanity.com/paper/2405.20126/) (2024)

## Abstract (arXiv)

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.