# Domain adaptation in small-scale and heterogeneous biological datasets

arXiv: https://arxiv.org/abs/2405.19221 · HTML: https://mlsanity.com/paper/2405.19221/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 71 of its 231 references that are in the map.
- First posted on arXiv 29 May 2024.

## Builds on (most cited first, 30 of 71)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer](https://mlsanity.com/paper/1701.06538/) (2017)
- [Domain-Adversarial Training of Neural Networks](https://mlsanity.com/paper/1505.07818/) (2015)
- [Unsupervised Domain Adaptation by Backpropagation](https://mlsanity.com/paper/1409.7495/) (2014)
- [Understanding Black-box Predictions via Influence Functions](https://mlsanity.com/paper/1703.04730/) (2017)
- [Moment Matching for Multi-Source Domain Adaptation](https://mlsanity.com/paper/1812.01754/) (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)
- [Explanation in Artificial Intelligence: Insights from the Social Sciences](https://mlsanity.com/paper/1706.07269/) (2017)
- [Deep CORAL: Correlation Alignment for Deep Domain Adaptation](https://mlsanity.com/paper/1607.01719/) (2016)
- [Deep Hashing Network for Unsupervised Domain Adaptation](https://mlsanity.com/paper/1706.07522/) (2017)
- [A Survey on Deep Learning in Medical Image Analysis](https://mlsanity.com/paper/1702.05747/) (2017)
- [A Closer Look at Memorization in Deep Networks](https://mlsanity.com/paper/1706.05394/) (2017)
- [TabNet: Attentive Interpretable Tabular Learning](https://mlsanity.com/paper/1908.07442/) (2019)
- [Deep Visual Domain Adaptation: A Survey](https://mlsanity.com/paper/1802.03601/) (2018)
- [Domain Generalization via Invariant Feature Representation](https://mlsanity.com/paper/1301.2115/) (2013)
- [Deep Transfer Learning with Joint Adaptation Networks](https://mlsanity.com/paper/1605.06636/) (2016)
- [Deep Domain Confusion: Maximizing for Domain Invariance](https://mlsanity.com/paper/1412.3474/) (2014)
- [Learning to Adapt Structured Output Space for Semantic Segmentation](https://mlsanity.com/paper/1802.10349/) (2018)
- [FDA: Fourier Domain Adaptation for Semantic Segmentation](https://mlsanity.com/paper/2004.05498/) (2020)
- [Contrastive Adaptation Network for Unsupervised Domain Adaptation](https://mlsanity.com/paper/1901.00976/) (2019)
- [Return of Frustratingly Easy Domain Adaptation](https://mlsanity.com/paper/1511.05547/) (2015)
- [A Survey of Unsupervised Deep Domain Adaptation](https://mlsanity.com/paper/1812.02849/) (2018)
- [Domain Adaptation for Medical Image Analysis: A Survey](https://mlsanity.com/paper/2102.09508/) (2021)
- [Semi-supervised Domain Adaptation via Minimax Entropy](https://mlsanity.com/paper/1904.06487/) (2019)
- [Domain Separation Networks](https://mlsanity.com/paper/1608.06019/) (2016)
- [Learning from Simulated and Unsupervised Images through Adversarial Training](https://mlsanity.com/paper/1612.07828/) (2016)
- [Domain Generalization for Object Recognition with Multi-task Autoencoders](https://mlsanity.com/paper/1508.07680/) (2015)
- [Characterizing and Avoiding Negative Transfer](https://mlsanity.com/paper/1811.09751/) (2018)
- [When Do Neural Nets Outperform Boosted Trees on Tabular Data?](https://mlsanity.com/paper/2305.02997/) (2023)

## Built on by (most cited first, 5 of 5)

- [UniTrans: A Unified Vertical Federated Knowledge Transfer Framework for Enhancing Cross-Hospital Collaboration](https://mlsanity.com/paper/2501.11388/) (2025)
- [Domain Adaptation-Enhanced Searchlight: Enabling classification of brain states from visual perception to mental imagery](https://mlsanity.com/paper/2408.01163/) (2024)
- [HyperTTA: Test-Time Adaptation for Hyperspectral Image Classification under Distribution Shifts](https://mlsanity.com/paper/2509.08436/) (2025)
- [Generalized few-shot transfer learning architecture for modeling the EDFA gain spectrum](https://mlsanity.com/paper/2507.21728/) (2025)
- [Simple Agents Outperform Experts in Biomedical Imaging Workflow Optimization](https://mlsanity.com/paper/2512.06006/) (2025)

## Abstract (arXiv)

Machine learning techniques are steadily becoming more important in modern biology, and are used to build predictive models, discover patterns, and investigate biological problems. However, models trained on one dataset are often not generalizable to other datasets from different cohorts or laboratories, due to differences in the statistical properties of these datasets. These could stem from technical differences, such as the measurement technique used, or from relevant biological differences between the populations studied. Domain adaptation, a type of transfer learning, can alleviate this problem by aligning the statistical distributions of features and samples among different datasets so that similar models can be applied across them. However, a majority of state-of-the-art domain adaptation methods are designed to work with large-scale data, mostly text and images, while biological datasets often suffer from small sample sizes, and possess complexities such as heterogeneity of the feature space. This Review aims to synthetically discuss domain adaptation methods in the context of small-scale and highly heterogeneous biological data. We describe the benefits and challenges of domain adaptation in biological research and critically discuss some of its objectives, strengths, and weaknesses through key representative methodologies. We argue for the incorporation of domain adaptation techniques to the computational biologist's toolkit, with further development of customized approaches.