@misc{adapt-python_adapt_2023,
	title = {{ADAPT} - {Awesome} {Domain} {Adaptation} {Python} {Toolbox}},
	copyright = {BSD-2-Clause},
	url = {https://github.com/adapt-python/adapt},
	abstract = {ADAPT is an open source library providing numerous tools to perform Transfer Learning and Domain Adaptation.

The purpose of the ADAPT library is to facilitate the access to transfer learning algorithms for a large public, including industrial players. ADAPT is specifically designed for Scikit-learn and Tensorflow users with a "user-friendly" approach. All objects in ADAPT implement the fit, predict and score methods like any scikit-learn object.},
	urldate = {2023-07-05},
	author = {adapt-python},
	month = jun,
	year = {2023},
	note = {original-date: 2020-06-25T08:08:21Z},
	keywords = {\#Code, \#Github, \#Transfer, /unread, adversarial-learning, adversarial-networks, dann, deep-learning, domain-adaptation, feature-selection, few-shot-learning, generalization, importance-sampling, machine-learning, python, regularization-parameters, scikit-learn, tensorflow, transfer-learning, zero-shot-learning},
}

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