Pith. sign in

REVIEW

Not yet reviewed by Pith; the record is open.

Re-run · record.json Download PDF Read on arXiv ↗

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2107.03049 v2 pith:HFENZ4S6 submitted 2021-07-07 cs.LG

ADAPT : Awesome Domain Adaptation Python Toolbox

Antoine de Mathelin , Mounir Atiq , Guillaume Richard , Alejandro de la Concha , Mouad Yachouti , Fran\c{c}ois Deheeger , Mathilde Mougeot , Nicolas Vayatis This is my paper
classification cs.LG
keywords adaptadaptationdomainlibrarypythonaccessadapt-pythonapproach
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
0 comments
read the original abstract

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.

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.