# Semi-supervised Domain Adaptation via Prototype-based Multi-level Learning

arXiv: https://arxiv.org/abs/2305.02693 · HTML: https://mlsanity.com/paper/2305.02693/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 27 references that are in the map.
- First posted on arXiv 4 May 2023.

## Builds on (most cited first, 22 of 22)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Domain-Adversarial Training of Neural Networks](https://mlsanity.com/paper/1505.07818/) (2015)
- [RandAugment: Practical automated data augmentation with a reduced search space](https://mlsanity.com/paper/1909.13719/) (2019)
- [FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence](https://mlsanity.com/paper/2001.07685/) (2020)
- [Moment Matching for Multi-Source Domain Adaptation](https://mlsanity.com/paper/1812.01754/) (2018)
- [Deep CORAL: Correlation Alignment for Deep Domain Adaptation](https://mlsanity.com/paper/1607.01719/) (2016)
- [Deep Hashing Network for Unsupervised Domain Adaptation](https://mlsanity.com/paper/1706.07522/) (2017)
- [Deep Transfer Learning with Joint Adaptation Networks](https://mlsanity.com/paper/1605.06636/) (2016)
- [VisDA: The Visual Domain Adaptation Challenge](https://mlsanity.com/paper/1710.06924/) (2017)
- [Semi-supervised Domain Adaptation via Minimax Entropy](https://mlsanity.com/paper/1904.06487/) (2019)
- [Joint Distribution Optimal Transportation for Domain Adaptation](https://mlsanity.com/paper/1705.08848/) (2017)
- [Multi-Adversarial Domain Adaptation](https://mlsanity.com/paper/1809.02176/) (2018)
- [Unbalanced minibatch Optimal Transport; applications to Domain Adaptation](https://mlsanity.com/paper/2103.03606/) (2021)
- [Attract, Perturb, and Explore: Learning a Feature Alignment Network for Semi-supervised Domain Adaptation](https://mlsanity.com/paper/2007.09375/) (2020)
- [Cross-Domain Adaptive Clustering for Semi-Supervised Domain Adaptation](https://mlsanity.com/paper/2104.09415/) (2021)
- [CLDA: Contrastive Learning for Semi-Supervised Domain Adaptation](https://mlsanity.com/paper/2107.00085/) (2021)
- [Semi-supervised Semantic Segmentation with Prototype-based Consistency Regularization](https://mlsanity.com/paper/2210.04388/) (2022)
- [Joint Domain Alignment and Discriminative Feature Learning for Unsupervised Deep Domain Adaptation](https://mlsanity.com/paper/1808.09347/) (2018)
- [Deep Co-Training with Task Decomposition for Semi-Supervised Domain Adaptation](https://mlsanity.com/paper/2007.12684/) (2020)
- [ECACL: A Holistic Framework for Semi-Supervised Domain Adaptation](https://mlsanity.com/paper/2104.09136/) (2021)
- [Multi-level Consistency Learning for Semi-supervised Domain Adaptation](https://mlsanity.com/paper/2205.04066/) (2022)
- [Con2DA: Simplifying Semi-supervised Domain Adaptation by Learning Consistent and Contrastive Feature Representations](https://mlsanity.com/paper/2204.01558/) (2022)

## Built on by (most cited first, 9 of 9)

- [Learning CNN on ViT: A Hybrid Model to Explicitly Class-specific Boundaries for Domain Adaptation](https://mlsanity.com/paper/2403.18360/) (2024)
- [Universal Semi-Supervised Domain Adaptation by Mitigating Common-Class Bias](https://mlsanity.com/paper/2403.11234/) (2024)
- [Learning from Different Samples: A Source-free Framework for Semi-supervised Domain Adaptation](https://mlsanity.com/paper/2411.06665/) (2024)
- [CRMSP: A Semi-supervised Approach for Key Information Extraction with Class-Rebalancing and Merged Semantic Pseudo-Labeling](https://mlsanity.com/paper/2407.15873/) (2024)
- [Unleashing the Power of Intermediate Domains for Mixed Domain Semi-Supervised Medical Image Segmentation](https://mlsanity.com/paper/2505.24567/) (2025)
- [Learning from Noisy Labels for Long-tailed Data via Optimal Transport](https://mlsanity.com/paper/2408.03977/) (2024)
- [Source-free Semantic Regularization Learning for Semi-supervised Domain Adaptation](https://mlsanity.com/paper/2501.01126/) (2025)
- [PHASE: Physiology-Aware Hyperspectral Reconstruction via Object-to-Human Domain Adaptation](https://mlsanity.com/paper/2511.13020/) (2025)
- [Conformal Risk Minimization for Semi-Supervised Domain Adaptation via Optimal Transport](https://mlsanity.com/paper/2608.23153/) (2026)

## Abstract (arXiv)

In semi-supervised domain adaptation (SSDA), a few labeled target samples of each class help the model to transfer knowledge representation from the fully labeled source domain to the target domain. Many existing methods ignore the benefits of making full use of the labeled target samples from multi-level. To make better use of this additional data, we propose a novel Prototype-based Multi-level Learning (ProML) framework to better tap the potential of labeled target samples. To achieve intra-domain adaptation, we first introduce a pseudo-label aggregation based on the intra-domain optimal transport to help the model align the feature distribution of unlabeled target samples and the prototype. At the inter-domain level, we propose a cross-domain alignment loss to help the model use the target prototype for cross-domain knowledge transfer. We further propose a dual consistency based on prototype similarity and linear classifier to promote discriminative learning of compact target feature representation at the batch level. Extensive experiments on three datasets, including DomainNet, VisDA2017, and Office-Home demonstrate that our proposed method achieves state-of-the-art performance in SSDA.