# ADAPT : Awesome Domain Adaptation Python Toolbox

arXiv: https://arxiv.org/abs/2107.03049 · HTML: https://mlsanity.com/paper/2107.03049/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 51 references that are in the map.
- First posted on arXiv 7 Jul 2021.

## Builds on (most cited first, 18 of 18)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Domain-Adversarial Training of Neural Networks](https://mlsanity.com/paper/1505.07818/) (2015)
- [Adversarial Discriminative Domain Adaptation](https://mlsanity.com/paper/1702.05464/) (2017)
- [Deep CORAL: Correlation Alignment for Deep Domain Adaptation](https://mlsanity.com/paper/1607.01719/) (2016)
- [In Search of Lost Domain Generalization](https://mlsanity.com/paper/2007.01434/) (2020)
- [Do We Really Need to Access the Source Data? Source Hypothesis Transfer for Unsupervised Domain Adaptation](https://mlsanity.com/paper/2002.08546/) (2020)
- [Maximum Classifier Discrepancy for Unsupervised Domain Adaptation](https://mlsanity.com/paper/1712.02560/) (2017)
- [Optimal Transport for Domain Adaptation](https://mlsanity.com/paper/1507.00504/) (2015)
- [Return of Frustratingly Easy Domain Adaptation](https://mlsanity.com/paper/1511.05547/) (2015)
- [Bridging Theory and Algorithm for Domain Adaptation](https://mlsanity.com/paper/1904.05801/) (2019)
- [Unified Deep Supervised Domain Adaptation and Generalization](https://mlsanity.com/paper/1709.10190/) (2017)
- [Domain Adaptive Ensemble Learning](https://mlsanity.com/paper/2003.07325/) (2020)
- [Domain Adversarial for Acoustic Emotion Recognition](https://mlsanity.com/paper/1804.07690/) (2018)
- [Adversarial Weighting for Domain Adaptation in Regression](https://mlsanity.com/paper/2006.08251/) (2020)
- [Understanding the Mechanisms of Deep Transfer Learning for Medical Images](https://mlsanity.com/paper/1704.06040/) (2017)
- [Nearest Neighbor-based Importance Weighting](https://mlsanity.com/paper/2102.02291/) (2021)
- [Learn on Source, Refine on Target:A Model Transfer Learning Framework with Random Forests](https://mlsanity.com/paper/1511.01258/) (2015)
- [Transfer Learning Toolkit: Primers and Benchmarks](https://mlsanity.com/paper/1911.08967/) (2019)

## Built on by (most cited first, 13 of 13)

- [Better, Not Just More: Data-Centric Machine Learning for Earth Observation](https://mlsanity.com/paper/2312.05327/) (2023)
- [Fast and Accurate Importance Weighting for Correcting Sample Bias](https://mlsanity.com/paper/2209.04215/) (2022)
- [Nearest Neighbor Sampling for Covariate Shift Adaptation](https://mlsanity.com/paper/2312.09969/) (2023)
- [A General Approach for Determining Applicability Domain of Machine Learning Models](https://mlsanity.com/paper/2406.05143/) (2024)
- [Unsupervised domain adaptation by learning using privileged information](https://mlsanity.com/paper/2303.09350/) (2023)
- [Detecting train driveshaft damages using accelerometer signals and Differential Convolutional Neural Networks](https://mlsanity.com/paper/2211.09011/) (2022)
- [Revisiting the Robustness of the Minimum Error Entropy Criterion: A Transfer Learning Case Study](https://mlsanity.com/paper/2307.08572/) (2023)
- [Domain Adaptation-Enhanced Searchlight: Enabling classification of brain states from visual perception to mental imagery](https://mlsanity.com/paper/2408.01163/) (2024)
- [PyTorch Adapt](https://mlsanity.com/paper/2211.15673/) (2022)
- [biquality-learn: a Python library for Biquality Learning](https://mlsanity.com/paper/2308.09643/) (2023)
- [Some notes concerning a generalized KMM-type optimization method for density ratio estimation](https://mlsanity.com/paper/2309.07887/) (2023)
- [Sample Selection Bias in Machine Learning for Healthcare](https://mlsanity.com/paper/2405.07841/) (2024)
- [Vertical Validation: Evaluating Implicit Generative Models for Graphs on Thin Support Regions](https://mlsanity.com/paper/2411.13358/) (2024)

## Abstract (arXiv)

In this paper, we introduce the ADAPT library, an open source Python API providing the implementation of the main transfer learning and domain adaptation methods. The library is designed with a user friendly approach to facilitate the access to domain adaptation for a wide public. ADAPT is compatible with scikit-learn and TensorFlow and a full documentation is proposed online https://adapt-python.github.io/adapt/ with a substantial gallery of examples.