# Unsupervised Domain Adaptation with Adversarial Residual Transform Networks

arXiv: https://arxiv.org/abs/1804.09578 · HTML: https://mlsanity.com/paper/1804.09578/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Apr 2018.

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

- [Domain Adaptation via Prompt Learning](https://mlsanity.com/paper/2202.06687/) (2022)
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- [Quantum Generative Diffusion Model: A Fully Quantum-Mechanical Model for Generating Quantum State Ensemble](https://mlsanity.com/paper/2401.07039/) (2024)
- [Instrumental Variable-Driven Domain Generalization with Unobserved Confounders](https://mlsanity.com/paper/2110.01438/) (2021)
- [Unsupervised Domain Adaptation for Low-dose CT Reconstruction via Bayesian Uncertainty Alignment](https://mlsanity.com/paper/2302.13251/) (2023)
- [Wavelet Regularization Benefits Adversarial Training](https://mlsanity.com/paper/2206.03727/) (2022)
- [Deep Transfer Learning for Kidney Cancer Diagnosis](https://mlsanity.com/paper/2408.04318/) (2024)
- [On the Robustness of Domain Adaption to Adversarial Attacks](https://mlsanity.com/paper/2108.01807/) (2021)
- [Hyperspectral Image Analysis in Single-Modal and Multimodal setting using Deep Learning Techniques](https://mlsanity.com/paper/2403.01546/) (2024)
- [Unsupervised Adaptive Semantic Segmentation with Local Lipschitz Constraint](https://mlsanity.com/paper/2105.12939/) (2021)
- [Weighted Joint Maximum Mean Discrepancy Enabled Multi-Source-Multi-Target Unsupervised Domain Adaptation Fault Diagnosis](https://mlsanity.com/paper/2310.14790/) (2023)
- [Unsupervised Attention Regularization Based Domain Adaptation for Oracle Character Recognition](https://mlsanity.com/paper/2409.15893/) (2024)
- [CrackUDA: Incremental Unsupervised Domain Adaptation for Improved Crack Segmentation in Civil Structures](https://mlsanity.com/paper/2412.15637/) (2024)
- [Test-Time Warmup for Multimodal Large Language Models](https://mlsanity.com/paper/2509.10641/) (2025)

## Abstract (arXiv)

Domain adaptation is widely used in learning problems lacking labels. Recent studies show that deep adversarial domain adaptation models can make markable improvements in performance, which include symmetric and asymmetric architectures. However, the former has poor generalization ability whereas the latter is very hard to train. In this paper, we propose a novel adversarial domain adaptation method named Adversarial Residual Transform Networks (ARTNs) to improve the generalization ability, which directly transforms the source features into the space of target features. In this model, residual connections are used to share features and adversarial loss is reconstructed, thus making the model more generalized and easier to train. Moreover, a special regularization term is added to the loss function to alleviate a vanishing gradient problem, which enables its training process stable. A series of experiments based on Amazon review dataset, digits datasets and Office-31 image datasets are conducted to show that the proposed ARTN can be comparable with the methods of the state-of-the-art.