# Towards Fair Knowledge Transfer for Imbalanced Domain Adaptation

arXiv: https://arxiv.org/abs/2010.12184 · HTML: https://mlsanity.com/paper/2010.12184/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Oct 2020.

## Built on by (most cited first, 7 of 7)

- [Transferring Fairness under Distribution Shifts via Fair Consistency Regularization](https://mlsanity.com/paper/2206.12796/) (2022)
- [On the Benefits of Attribute-Driven Graph Domain Adaptation](https://mlsanity.com/paper/2502.06808/) (2025)
- [Inverse Image Frequency for Long-tailed Image Recognition](https://mlsanity.com/paper/2209.04861/) (2022)
- [Adversarial Bi-Regressor Network for Domain Adaptive Regression](https://mlsanity.com/paper/2209.09943/) (2022)
- [Dr. FERMI: A Stochastic Distributionally Robust Fair Empirical Risk Minimization Framework](https://mlsanity.com/paper/2309.11682/) (2023)
- [centroIDA: Cross-Domain Class Discrepancy Minimization Based on Accumulative Class-Centroids for Imbalanced Domain Adaptation](https://mlsanity.com/paper/2308.10619/) (2023)
- [iBARLE: imBalance-Aware Room Layout Estimation](https://mlsanity.com/paper/2308.15050/) (2023)

## Abstract (arXiv)

Domain adaptation (DA) becomes an up-and-coming technique to address the insufficient or no annotation issue by exploiting external source knowledge. Existing DA algorithms mainly focus on practical knowledge transfer through domain alignment. Unfortunately, they ignore the fairness issue when the auxiliary source is extremely imbalanced across different categories, which results in severe under-presented knowledge adaptation of minority source set. To this end, we propose a Towards Fair Knowledge Transfer (TFKT) framework to handle the fairness challenge in imbalanced cross-domain learning. Specifically, a novel cross-domain mixup generation is exploited to augment the minority source set with target information to enhance fairness. Moreover, dual distinct classifiers and cross-domain prototype alignment are developed to seek a more robust classifier boundary and mitigate the domain shift. Such three strategies are formulated into a unified framework to address the fairness issue and domain shift challenge. Extensive experiments over two popular benchmarks have verified the effectiveness of our proposed model by comparing to existing state-of-the-art DA models, and especially our model significantly improves over 20% on two benchmarks in terms of the overall accuracy.