# Feature-Level Domain Adaptation

arXiv: https://arxiv.org/abs/1512.04829 · HTML: https://mlsanity.com/paper/1512.04829/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Dec 2015.

## Built on by (most cited first, 8 of 8)

- [Domain Adaptive Fake News Detection via Reinforcement Learning](https://mlsanity.com/paper/2202.08159/) (2022)
- [Attribute Descent: Simulating Object-Centric Datasets on the Content Level and Beyond](https://mlsanity.com/paper/2202.14034/) (2022)
- [Image-based Navigation in Real-World Environments via Multiple Mid-level Representations: Fusion Models, Benchmark and Efficient Evaluation](https://mlsanity.com/paper/2202.01069/) (2022)
- [DCAST: Diverse Class-Aware Self-Training Mitigates Selection Bias for Fairer Learning](https://mlsanity.com/paper/2409.20126/) (2024)
- [Stratified Learning: A General-Purpose Statistical Method for Improved Learning under Covariate Shift](https://mlsanity.com/paper/2106.11211/) (2021)
- [Direct domain adaptation through reciprocal linear transformations](https://mlsanity.com/paper/2108.07600/) (2021)
- [Towards Backwards-Compatible Data with Confounded Domain Adaptation](https://mlsanity.com/paper/2203.12720/) (2022)
- [Metric-DST: Mitigating Selection Bias Through Diversity-Guided Semi-Supervised Metric Learning](https://mlsanity.com/paper/2411.18442/) (2024)

## Abstract (arXiv)

Domain adaptation is the supervised learning setting in which the training and test data are sampled from different distributions: training data is sampled from a source domain, whilst test data is sampled from a target domain. This paper proposes and studies an approach, called feature-level domain adaptation (FLDA), that models the dependence between the two domains by means of a feature-level transfer model that is trained to describe the transfer from source to target domain. Subsequently, we train a domain-adapted classifier by minimizing the expected loss under the resulting transfer model. For linear classifiers and a large family of loss functions and transfer models, this expected loss can be computed or approximated analytically, and minimized efficiently. Our empirical evaluation of FLDA focuses on problems comprising binary and count data in which the transfer can be naturally modeled via a dropout distribution, which allows the classifier to adapt to differences in the marginal probability of features in the source and the target domain. Our experiments on several real-world problems show that FLDA performs on par with state-of-the-art domain-adaptation techniques.