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ADAPT

swMATH ID: 39360
Software Authors: Antoine de Mathelin, François Deheeger, Guillaume Richard, Mathilde Mougeot, Nicolas Vayatis
Description: ADAPT : Awesome Domain Adaptation Python Toolbox. ADAPT is an open-source python library providing the implementation of several domain adaptation methods. The library is suited for scikit-learn estimator object (object which implement fit and predict methods) and tensorflow models. Most of the implemented methods are developed in an estimator agnostic fashion, offering various possibilities adapted to multiple usage. The library offers three modules corresponding to the three principal strategies of domain adaptation: (i) feature-based containing methods performing feature transformation; (ii) instance-based with the implementation of reweighting techniques and (iii) parameter-based proposing methods to adapt pre-trained models to novel observations. A full documentation is proposed online this https URL with gallery of examples. Besides, the library presents an high test coverage.
Homepage: https://adapt-python.github.io/adapt/
Keywords: Machine Learning; arXiv_cs.LG; ADAPT; Python Toolbox; domain adaptation; Transfer learning; Deep networks; Importance weighting; Fine tuning; arXiv_publication
Related Software: TensorFlow; Transfer learning; salad; dalib; PyTorch; Scikit; Python
Referenced in: 0 Publications

Standard Articles

1 Publication describing the Software Year
ADAPT : Awesome Domain Adaptation Python Toolbox
Antoine de Mathelin, François Deheeger, Guillaume Richard, Mathilde Mougeot, Nicolas Vayatis
2021