# Learning Target Domain Specific Classifier for Partial Domain Adaptation

arXiv: https://arxiv.org/abs/2008.10785 · HTML: https://mlsanity.com/paper/2008.10785/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Aug 2020.

## Built on by (most cited first, 16 of 16)

- [Unsupervised Domain Adaption Harnessing Vision-Language Pre-training](https://mlsanity.com/paper/2408.02192/) (2024)
- [Cross-Site Severity Assessment of COVID-19 from CT Images via Domain Adaptation](https://mlsanity.com/paper/2109.03478/) (2021)
- [Trust your Good Friends: Source-free Domain Adaptation by Reciprocal Neighborhood Clustering](https://mlsanity.com/paper/2309.00528/) (2023)
- [RefRec: Pseudo-labels Refinement via Shape Reconstruction for Unsupervised 3D Domain Adaptation](https://mlsanity.com/paper/2110.11036/) (2021)
- [SPNet: A novel deep neural network for retinal vessel segmentation based on shared decoder and pyramid-like loss](https://mlsanity.com/paper/2202.09515/) (2022)
- [Maximizing Conditional Independence for Unsupervised Domain Adaptation](https://mlsanity.com/paper/2203.03212/) (2022)
- [Adversarially Domain-adaptive Latent Diffusion for Unsupervised Semantic Segmentation](https://mlsanity.com/paper/2412.16859/) (2024)
- [Geometry-Aware Unsupervised Domain Adaptation](https://mlsanity.com/paper/2112.11041/) (2021)
- [Learning to Discover Knowledge: A Weakly-Supervised Partial Domain Adaptation Approach](https://mlsanity.com/paper/2406.14274/) (2024)
- [A Unified Framework for Iris Anti-Spoofing: Introducing Iris Anti-Spoofing Cross-Domain-Testing Protocol and Masked-MoE Method](https://mlsanity.com/paper/2408.09752/) (2024)
- [BiPC: Bidirectional Probability Calibration for Unsupervised Domain Adaption](https://mlsanity.com/paper/2409.19542/) (2024)
- [GrabDAE: An Innovative Framework for Unsupervised Domain Adaptation Utilizing Grab-Mask and Denoise Auto-Encoder](https://mlsanity.com/paper/2410.08023/) (2024)
- [SciceVPR: Stable Cross-Image Correlation Enhanced Model for Visual Place Recognition](https://mlsanity.com/paper/2502.20676/) (2025)
- [Soft-Masked Semi-Dual Optimal Transport for Partial Domain Adaptation](https://mlsanity.com/paper/2505.01664/) (2025)
- [Bi-level Unbalanced Optimal Transport for Partial Domain Adaptation](https://mlsanity.com/paper/2506.08020/) (2025)
- [Cross-Domain Attribute Alignment with CLIP: A Rehearsal-Free Approach for Class-Incremental Unsupervised Domain Adaptation](https://mlsanity.com/paper/2509.11264/) (2025)

## Abstract (arXiv)

Unsupervised domain adaptation~(UDA) aims at reducing the distribution discrepancy when transferring knowledge from a labeled source domain to an unlabeled target domain. Previous UDA methods assume that the source and target domains share an identical label space, which is unrealistic in practice since the label information of the target domain is agnostic. This paper focuses on a more realistic UDA scenario, i.e. partial domain adaptation (PDA), where the target label space is subsumed to the source label space. In the PDA scenario, the source outliers that are absent in the target domain may be wrongly matched to the target domain (technically named negative transfer), leading to performance degradation of UDA methods. This paper proposes a novel Target Domain Specific Classifier Learning-based Domain Adaptation (TSCDA) method. TSCDA presents a soft-weighed maximum mean discrepancy criterion to partially align feature distributions and alleviate negative transfer. Also, it learns a target-specific classifier for the target domain with pseudo-labels and multiple auxiliary classifiers, to further address classifier shift. A module named Peers Assisted Learning is used to minimize the prediction difference between multiple target-specific classifiers, which makes the classifiers more discriminant for the target domain. Extensive experiments conducted on three PDA benchmark datasets show that TSCDA outperforms other state-of-the-art methods with a large margin, e.g. 4% and 5.6% averagely on Office-31 and Office-Home, respectively.