# Weakly-Supervised Domain Adaptation with Proportion-Constrained Pseudo-Labeling

arXiv: https://arxiv.org/abs/2506.22301 · HTML: https://mlsanity.com/paper/2506.22301/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 34 references that are in the map.
- First posted on arXiv 27 Jun 2025.

## Builds on (most cited first, 25 of 25)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Domain-Adversarial Training of Neural Networks](https://mlsanity.com/paper/1505.07818/) (2015)
- [Adversarial Discriminative Domain Adaptation](https://mlsanity.com/paper/1702.05464/) (2017)
- [Maximum Classifier Discrepancy for Unsupervised Domain Adaptation](https://mlsanity.com/paper/1712.02560/) (2017)
- [Contrastive Adaptation Network for Unsupervised Domain Adaptation](https://mlsanity.com/paper/1901.00976/) (2019)
- [Unsupervised Domain Adaptation with Residual Transfer Networks](https://mlsanity.com/paper/1602.04433/) (2016)
- [Semi-supervised Domain Adaptation via Minimax Entropy](https://mlsanity.com/paper/1904.06487/) (2019)
- [Unsupervised Intra-domain Adaptation for Semantic Segmentation through Self-Supervision](https://mlsanity.com/paper/2004.07703/) (2020)
- [FixBi: Bridging Domain Spaces for Unsupervised Domain Adaptation](https://mlsanity.com/paper/2011.09230/) (2020)
- [Cross-Domain Adaptive Clustering for Semi-Supervised Domain Adaptation](https://mlsanity.com/paper/2104.09415/) (2021)
- [Learning from Label Proportions with Consistency Regularization](https://mlsanity.com/paper/1910.13188/) (2019)
- [Learning from Label Proportions by Learning with Label Noise](https://mlsanity.com/paper/2203.02496/) (2022)
- [Co-training for Demographic Classification Using Deep Learning from Label Proportions](https://mlsanity.com/paper/1709.04108/) (2017)
- [Semi-Supervised Domain Adaptation with Source Label Adaptation](https://mlsanity.com/paper/2302.02335/) (2023)
- [Semi-supervised Domain Adaptation via Sample-to-Sample Self-Distillation](https://mlsanity.com/paper/2111.14353/) (2021)
- [Two-stage Training for Learning from Label Proportions](https://mlsanity.com/paper/2105.10635/) (2021)
- [Class Distance Weighted Cross-Entropy Loss for Ulcerative Colitis Severity Estimation](https://mlsanity.com/paper/2202.05167/) (2022)
- [Divergence Optimization for Noisy Universal Domain Adaptation](https://mlsanity.com/paper/2104.00246/) (2021)
- [Learning from Label Proportion with Online Pseudo-Label Decision by Regret Minimization](https://mlsanity.com/paper/2302.08947/) (2023)
- [Weakly Supervised Open-set Domain Adaptation by Dual-domain Collaboration](https://mlsanity.com/paper/1904.13179/) (2019)
- [MixBag: Bag-Level Data Augmentation for Learning from Label Proportions](https://mlsanity.com/paper/2308.08822/) (2023)
- [Learning from Partial Label Proportions for Whole Slide Image Segmentation](https://mlsanity.com/paper/2405.09041/) (2024)
- [Weakly Supervised Test-Time Domain Adaptation for Object Detection](https://mlsanity.com/paper/2407.05607/) (2024)
- [Theoretical Proportion Label Perturbation for Learning from Label Proportions in Large Bags](https://mlsanity.com/paper/2408.14130/) (2024)

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

- [Leveraging Label Proportion Prior for Class-Imbalanced Semi-Supervised Learning](https://mlsanity.com/paper/2603.02957/) (2026)
- [Improving Category-Aware Active Domain Adaptation via Uncertainty-Guided Region Coverage](https://mlsanity.com/paper/2610.19803/) (2026)

## Abstract (arXiv)

Domain shift is a significant challenge in machine learning, particularly in medical applications where data distributions differ across institutions due to variations in data collection practices, equipment, and procedures. This can degrade performance when models trained on source domain data are applied to the target domain. Domain adaptation methods have been widely studied to address this issue, but most struggle when class proportions between the source and target domains differ. In this paper, we propose a weakly-supervised domain adaptation method that leverages class proportion information from the target domain, which is often accessible in medical datasets through prior knowledge or statistical reports. Our method assigns pseudo-labels to the unlabeled target data based on class proportion (called proportion-constrained pseudo-labeling), improving performance without the need for additional annotations. Experiments on two endoscopic datasets demonstrate that our method outperforms semi-supervised domain adaptation techniques, even when 5% of the target domain is labeled. Additionally, the experimental results with noisy proportion labels highlight the robustness of our method, further demonstrating its effectiveness in real-world application scenarios.