# Heterogeneous Domain Adaptation via Soft Transfer Network

arXiv: https://arxiv.org/abs/1908.10552 · HTML: https://mlsanity.com/paper/1908.10552/

## Key facts

- Cited by 14 papers in the map (data through 7 Oct 2026).
- First posted on arXiv 28 Aug 2019.

## Built on by (most cited first, 14 of 14)

- [Cross-Modal Fine-Tuning: Align then Refine](https://mlsanity.com/paper/2302.05738/) (2023)
- [Deep transfer learning for intrusion detection in industrial control networks: A comprehensive review](https://mlsanity.com/paper/2304.10550/) (2023)
- [A Recent Survey of Heterogeneous Transfer Learning](https://mlsanity.com/paper/2310.08459/) (2023)
- [Jacobian Norm for Unsupervised Source-Free Domain Adaptation](https://mlsanity.com/paper/2204.03467/) (2022)
- [Joint Semantic Transfer Network for IoT Intrusion Detection](https://mlsanity.com/paper/2210.15911/) (2022)
- [Rethinking Guidance Information to Utilize Unlabeled Samples:A Label Encoding Perspective](https://mlsanity.com/paper/2406.02862/) (2024)
- [Positive-Unlabeled Domain Adaptation](https://mlsanity.com/paper/2202.05695/) (2022)
- [Heterogeneous Domain Adaptation for IoT Intrusion Detection: A Geometric Graph Alignment Approach](https://mlsanity.com/paper/2301.09801/) (2023)
- [A Framework for Supervised Heterogeneous Transfer Learning using Dynamic Distribution Adaptation and Manifold Regularization](https://mlsanity.com/paper/2108.12293/) (2021)
- [Keypoint-Guided Optimal Transport: Models, Algorithms, and Applications](https://mlsanity.com/paper/2303.13102/) (2023)
- [Rethinking Cross-Modal Fine-Tuning: Optimizing the Interaction Between Feature Alignment and Target Fitting](https://mlsanity.com/paper/2601.18231/) (2026)
- [Semi Supervised Heterogeneous Domain Adaptation via Disentanglement and Pseudo-Labelling](https://mlsanity.com/paper/2406.14087/) (2024)
- [Open-Set Heterogeneous Domain Adaptation: Theoretical Analysis and Algorithm](https://mlsanity.com/paper/2412.13036/) (2024)
- [Semi-Supervised Noise Adaptation: Transferring Knowledge from Noise Domain](https://mlsanity.com/paper/2606.00558/) (2026)

## Abstract (arXiv)

Heterogeneous domain adaptation (HDA) aims to facilitate the learning task in a target domain by borrowing knowledge from a heterogeneous source domain. In this paper, we propose a Soft Transfer Network (STN), which jointly learns a domain-shared classifier and a domain-invariant subspace in an end-to-end manner, for addressing the HDA problem. The proposed STN not only aligns the discriminative directions of domains but also matches both the marginal and conditional distributions across domains. To circumvent negative transfer, STN aligns the conditional distributions by using the soft-label strategy of unlabeled target data, which prevents the hard assignment of each unlabeled target data to only one category that may be incorrect. Further, STN introduces an adaptive coefficient to gradually increase the importance of the soft-labels since they will become more and more accurate as the number of iterations increases. We perform experiments on the transfer tasks of image-to-image, text-to-image, and text-to-text. Experimental results testify that the STN significantly outperforms several state-of-the-art approaches.