# Model Adaptation: Unsupervised Domain Adaptation without Source Data

arXiv: https://arxiv.org/abs/2502.19316 · HTML: https://mlsanity.com/paper/2502.19316/

## Key facts

- Cited by 34 papers in the map (data through 14 Oct 2026).
- Builds on 43 of its 66 references that are in the map.
- First posted on arXiv 26 Feb 2025.

## Builds on (most cited first, 30 of 43)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)
- [Domain-Adversarial Training of Neural Networks](https://mlsanity.com/paper/1505.07818/) (2015)
- [Learning without Forgetting](https://mlsanity.com/paper/1606.09282/) (2016)
- [Conditional Generative Adversarial Nets](https://mlsanity.com/paper/1411.1784/) (2014)
- [Unsupervised Domain Adaptation by Backpropagation](https://mlsanity.com/paper/1409.7495/) (2014)
- [Spectral Normalization for Generative Adversarial Networks](https://mlsanity.com/paper/1802.05957/) (2018)
- [Learning Transferable Features with Deep Adaptation Networks](https://mlsanity.com/paper/1502.02791/) (2015)
- [Adversarial Discriminative Domain Adaptation](https://mlsanity.com/paper/1702.05464/) (2017)
- [How transferable are features in deep neural networks?](https://mlsanity.com/paper/1411.1792/) (2014)
- [Deep CORAL: Correlation Alignment for Deep Domain Adaptation](https://mlsanity.com/paper/1607.01719/) (2016)
- [CyCADA: Cycle-Consistent Adversarial Domain Adaptation](https://mlsanity.com/paper/1711.03213/) (2017)
- [Virtual Adversarial Training: A Regularization Method for Supervised and Semi-Supervised Learning](https://mlsanity.com/paper/1704.03976/) (2017)
- [InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets](https://mlsanity.com/paper/1606.03657/) (2016)
- [Maximum Classifier Discrepancy for Unsupervised Domain Adaptation](https://mlsanity.com/paper/1712.02560/) (2017)
- [Deep Transfer Learning with Joint Adaptation Networks](https://mlsanity.com/paper/1605.06636/) (2016)
- [Unsupervised Image-to-Image Translation Networks](https://mlsanity.com/paper/1703.00848/) (2017)
- [Deep Domain Confusion: Maximizing for Domain Invariance](https://mlsanity.com/paper/1412.3474/) (2014)
- [Confidence Regularized Self-Training](https://mlsanity.com/paper/1908.09822/) (2019)
- [Unsupervised Domain Adaptation with Residual Transfer Networks](https://mlsanity.com/paper/1602.04433/) (2016)
- [Domain Separation Networks](https://mlsanity.com/paper/1608.06019/) (2016)
- [DualGAN: Unsupervised Dual Learning for Image-to-Image Translation](https://mlsanity.com/paper/1704.02510/) (2017)
- [Unsupervised Pixel-Level Domain Adaptation with Generative Adversarial Networks](https://mlsanity.com/paper/1612.05424/) (2016)
- [Learning from Simulated and Unsupervised Images through Adversarial Training](https://mlsanity.com/paper/1612.07828/) (2016)
- [Learning to Discover Cross-Domain Relations with Generative Adversarial Networks](https://mlsanity.com/paper/1703.05192/) (2017)
- [Learning without Memorizing](https://mlsanity.com/paper/1811.08051/) (2018)
- [Sliced Wasserstein Discrepancy for Unsupervised Domain Adaptation](https://mlsanity.com/paper/1903.04064/) (2019)
- [Coupled Generative Adversarial Networks](https://mlsanity.com/paper/1606.07536/) (2016)

## Built on by (most cited first, 30 of 34)

- [Handling Out-of-Distribution Data: A Survey](https://mlsanity.com/paper/2507.21160/) (2025)
- [Progressive Test Time Energy Adaptation for Medical Image Segmentation](https://mlsanity.com/paper/2503.16616/) (2025)
- [LiDAR Remote Sensing Meets Weak Supervision: Concepts, Methods, and Perspectives](https://mlsanity.com/paper/2503.18384/) (2025)
- [ETA: Energy-based Test-time Adaptation for Depth Completion](https://mlsanity.com/paper/2508.05989/) (2025)
- [When and Where to Reset Matters for Long-Term Test-Time Adaptation](https://mlsanity.com/paper/2603.03796/) (2026)
- [Adapt in the Wild: Test-Time Entropy Minimization with Sharpness and Feature Regularization](https://mlsanity.com/paper/2509.04977/) (2025)
- [Towards Practical Emotion Recognition: An Unsupervised Source-Free Approach for EEG Domain Adaptation](https://mlsanity.com/paper/2504.03707/) (2025)
- [Co-STAR: Collaborative Curriculum Self-Training with Adaptive Regularization for Source-Free Video Domain Adaptation](https://mlsanity.com/paper/2504.11669/) (2025)
- [Temporal Restoration and Spatial Rewiring for Source-Free Multivariate Time Series Domain Adaptation](https://mlsanity.com/paper/2505.21525/) (2025)
- [Efficient Test-Time Adaptation through Latent Subspace Coefficients Search](https://mlsanity.com/paper/2510.11068/) (2025)
- [An Investigation of Test-time Adaptation for Audio Classification under Background Noise](https://mlsanity.com/paper/2507.15523/) (2025)
- [Consistent Assistant Domains Transformer for Source-free Domain Adaptation](https://mlsanity.com/paper/2510.01559/) (2025)
- [Source-Free Domain Adaptation for Geospatial Point Cloud Semantic Segmentation](https://mlsanity.com/paper/2601.08375/) (2026)
- [Source-Free Domain Adaptation by Optimizing Batch-Wise Cosine Similarity](https://mlsanity.com/paper/2601.17408/) (2026)
- [Bridging Domain Expertise and Generalization for Performance Estimation](https://mlsanity.com/paper/2606.06335/) (2026)
- [AdaptiveCDM: Source-Free Few-Shot Domain Adaptation for Cell Detection in Microscopic Images](https://mlsanity.com/paper/2503.03370/) (2025)
- [Shuffle PatchMix Augmentation with Confidence-Margin Weighted Pseudo-Labels for Enhanced Source-Free Domain Adaptation](https://mlsanity.com/paper/2505.24216/) (2025)
- [Model Recycling Framework for Multi-Source Data-Free Supervised Transfer Learning](https://mlsanity.com/paper/2508.02039/) (2025)
- [Rethinking the Backbone in Class Imbalanced Federated Source Free Domain Adaptation: The Utility of Vision Foundation Models](https://mlsanity.com/paper/2509.08372/) (2025)
- [Source-Free Domain Adaptive Semantic Segmentation of Remote Sensing Images with Diffusion-Guided Label Enrichment](https://mlsanity.com/paper/2509.18502/) (2025)
- [Vicinity-Guided Discriminative Latent Diffusion for Privacy-Preserving Domain Adaptation](https://mlsanity.com/paper/2510.00478/) (2025)
- [Black-Box Time-Series Domain Adaptation via Cross-Prompt Foundation Models](https://mlsanity.com/paper/2510.00487/) (2025)
- [Source-Free Cross-Domain Continual Learning](https://mlsanity.com/paper/2510.01649/) (2025)
- [Robust Source-Free Domain Adaptation for Medical Image Segmentation based on Curriculum Learning](https://mlsanity.com/paper/2510.08393/) (2025)
- [Attention Residual Fusion Network with Contrast for Source-free Domain Adaptation](https://mlsanity.com/paper/2510.22142/) (2025)
- [Uncover and Unlearn Nuisances: Agnostic Fully Test-Time Adaptation](https://mlsanity.com/paper/2511.12491/) (2025)
- [ALDI-ray: Adapting the ALDI Framework for Security X-ray Object Detection](https://mlsanity.com/paper/2512.02696/) (2025)
- [Progressive Conditioned Scale-Shift Recalibration of Self-Attention for Online Test-time Adaptation](https://mlsanity.com/paper/2512.12673/) (2025)
- [A Source-Free Approach for Domain Adaptation via Multiview Image Transformation and Latent Space Consistency](https://mlsanity.com/paper/2601.20284/) (2026)
- [Closing the Confusion Loop: CLIP-Guided Alignment for Source-Free Domain Adaptation](https://mlsanity.com/paper/2602.08730/) (2026)

## Abstract (arXiv)

In this paper, we investigate a challenging unsupervised domain adaptation setting -- unsupervised model adaptation. We aim to explore how to rely only on unlabeled target data to improve performance of an existing source prediction model on the target domain, since labeled source data may not be available in some real-world scenarios due to data privacy issues. For this purpose, we propose a new framework, which is referred to as collaborative class conditional generative adversarial net to bypass the dependence on the source data. Specifically, the prediction model is to be improved through generated target-style data, which provides more accurate guidance for the generator. As a result, the generator and the prediction model can collaborate with each other without source data. Furthermore, due to the lack of supervision from source data, we propose a weight constraint that encourages similarity to the source model. A clustering-based regularization is also introduced to produce more discriminative features in the target domain. Compared to conventional domain adaptation methods, our model achieves superior performance on multiple adaptation tasks with only unlabeled target data, which verifies its effectiveness in this challenging setting.