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arXiv 2107.03049cs.LG
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ADAPT:非常棒的领域自适应Python教程

ADAPT : Awesome Domain Adaptation Python Toolbox

Manufacture Française des pneumatiques Michelin(法国米其林轮胎制造公司);Centre Borelli(博雷利中心);Université Paris-Saclay(巴黎萨克雷大学);CNRS(法国国家科学研究中心);ENS Paris-Saclay(巴黎萨克雷高等师范学院)

Antoine de Mathelin, Mounir Atiq, Guillaume Richard, Alejandro de la Concha, Mouad Yachouti, François Deheeger, Mathilde Mougeot, Nicolas Vayatis

摘要:

在本文中,我们介绍了ADAPT库,这是一个开源的Python API,提供了主要的迁移学习和领域自适应方法的实现。图书馆的设计采用了用户友好的方法,以方便广大公众访问领域自适应。ADAPT与scikit-learn和TensorFlow兼容,并在https://adapt-python.github.io/adapt/上提供了完整的文档,其中包含大量的示例库。

英文摘要:

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.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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