# Selective Transfer with Reinforced Transfer Network for Partial Domain Adaptation

arXiv: https://arxiv.org/abs/1905.10756 · HTML: https://mlsanity.com/paper/1905.10756/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 May 2019.

## Built on by (most cited first, 11 of 11)

- [Generalizable Representation Learning for Mixture Domain Face Anti-Spoofing](https://mlsanity.com/paper/2105.02453/) (2021)
- [Aggregating From Multiple Target-Shifted Sources](https://mlsanity.com/paper/2105.04051/) (2021)
- [From Big to Small: Adaptive Learning to Partial-Set Domains](https://mlsanity.com/paper/2203.07375/) (2022)
- [Trust your Good Friends: Source-free Domain Adaptation by Reciprocal Neighborhood Clustering](https://mlsanity.com/paper/2309.00528/) (2023)
- [Context-aware Domain Adaptation for Time Series Anomaly Detection](https://mlsanity.com/paper/2304.07453/) (2023)
- [Memory-Assisted Sub-Prototype Mining for Universal Domain Adaptation](https://mlsanity.com/paper/2310.05453/) (2023)
- [Optimal transport meets noisy label robust loss and MixUp regularization for domain adaptation](https://mlsanity.com/paper/2206.11180/) (2022)
- [A Unified Framework for Unsupervised Domain Adaptation based on Instance Weighting](https://mlsanity.com/paper/2312.05024/) (2023)
- [GeT: Generative Target Structure Debiasing for Domain Adaptation](https://mlsanity.com/paper/2308.10205/) (2023)
- [Learning to Discover Knowledge: A Weakly-Supervised Partial Domain Adaptation Approach](https://mlsanity.com/paper/2406.14274/) (2024)
- [Soft-Masked Semi-Dual Optimal Transport for Partial Domain Adaptation](https://mlsanity.com/paper/2505.01664/) (2025)

## Abstract (arXiv)

One crucial aspect of partial domain adaptation (PDA) is how to select the relevant source samples in the shared classes for knowledge transfer. Previous PDA methods tackle this problem by re-weighting the source samples based on their high-level information (deep features). However, since the domain shift between source and target domains, only using the deep features for sample selection is defective. We argue that it is more reasonable to additionally exploit the pixel-level information for PDA problem, as the appearance difference between outlier source classes and target classes is significantly large. In this paper, we propose a reinforced transfer network (RTNet), which utilizes both high-level and pixel-level information for PDA problem. Our RTNet is composed of a reinforced data selector (RDS) based on reinforcement learning (RL), which filters out the outlier source samples, and a domain adaptation model which minimizes the domain discrepancy in the shared label space. Specifically, in the RDS, we design a novel reward based on the reconstruct errors of selected source samples on the target generator, which introduces the pixel-level information to guide the learning of RDS. Besides, we develope a state containing high-level information, which used by the RDS for sample selection. The proposed RDS is a general module, which can be easily integrated into existing DA models to make them fit the PDA situation. Extensive experiments indicate that RTNet can achieve state-of-the-art performance for PDA tasks on several benchmark datasets.