# Balanced Distribution Adaptation for Transfer Learning

arXiv: https://arxiv.org/abs/1807.00516 · HTML: https://mlsanity.com/paper/1807.00516/

## Key facts

- Cited by 32 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Jul 2018.

## Built on by (most cited first, 30 of 32)

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- [Contrastive Conditional Alignment based on Label Shift Calibration for Imbalanced Domain Adaptation](https://mlsanity.com/paper/2412.20337/) (2024)
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- [A Framework for Supervised Heterogeneous Transfer Learning using Dynamic Distribution Adaptation and Manifold Regularization](https://mlsanity.com/paper/2108.12293/) (2021)
- [Instance-level Heterogeneous Domain Adaptation for Limited-labeled Sketch-to-Photo Retrieval](https://mlsanity.com/paper/2211.14515/) (2022)
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- [Investigate the Essence of Long-Tailed Recognition from a Unified Perspective](https://mlsanity.com/paper/2107.03758/) (2021)
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- [FV-UPatches: Enhancing Universality in Finger Vein Recognition](https://mlsanity.com/paper/2206.01061/) (2022)
- [SuDA: Support-based Domain Adaptation for Sim2Real Motion Capture with Flexible Sensors](https://mlsanity.com/paper/2405.16152/) (2024)
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- [Distribution alignment based transfer fusion frameworks on quantum devices for seeking quantum advantages](https://mlsanity.com/paper/2411.01822/) (2024)
- [Theoretically Guaranteed Distribution Adaptable Learning](https://mlsanity.com/paper/2411.02921/) (2024)

## Abstract (arXiv)

Transfer learning has achieved promising results by leveraging knowledge from the source domain to annotate the target domain which has few or none labels. Existing methods often seek to minimize the distribution divergence between domains, such as the marginal distribution, the conditional distribution or both. However, these two distances are often treated equally in existing algorithms, which will result in poor performance in real applications. Moreover, existing methods usually assume that the dataset is balanced, which also limits their performances on imbalanced tasks that are quite common in real problems. To tackle the distribution adaptation problem, in this paper, we propose a novel transfer learning approach, named as Balanced Distribution underlineAdaptation~(BDA), which can adaptively leverage the importance of the marginal and conditional distribution discrepancies, and several existing methods can be treated as special cases of BDA. Based on BDA, we also propose a novel Weighted Balanced Distribution Adaptation~(W-BDA) algorithm to tackle the class imbalance issue in transfer learning. W-BDA not only considers the distribution adaptation between domains but also adaptively changes the weight of each class. To evaluate the proposed methods, we conduct extensive experiments on several transfer learning tasks, which demonstrate the effectiveness of our proposed algorithms over several state-of-the-art methods.