• ADAPT : Awesome Domain Adaptat...

Treffer: ADAPT : Awesome Domain Adaptation Python Toolbox

Title:
ADAPT : Awesome Domain Adaptation Python Toolbox
Authors:
de Mathelin, Antoine, Atiq, Mounir, Richard, Guillaume, de la Concha, Alejandro, Yachouti, Mouad, Deheeger, François, Mougeot, Mathilde, Vayatis, Nicolas
Contributors:
CB - Centre Borelli - UMR 9010 (CB), Service de Santé des Armées-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Ecole Normale Supérieure Paris-Saclay (ENS Paris Saclay)-Université Paris Cité (UPCité), Manufacture Française des Pneumatiques Michelin (Michelin)
Source:
https://hal.science/hal-04455951 ; 2024.
Publisher Information:
CCSD
Publication Year:
2024
Subject Terms:
Domain Adaptation Transfer learning Deep networks Importance weighting Fine tuning Machine learning Python, Domain Adaptation, Transfer learning, Deep networks, Importance weighting, Fine tuning, Machine learning, Python, [INFO]Computer Science [cs]
Document Type:
Report report
Language:
English
Relation:
info:eu-repo/semantics/altIdentifier/arxiv/2107.03049; ARXIV: 2107.03049
Availability:
https://hal.science/hal-04455951
https://hal.science/hal-04455951v1/document
https://hal.science/hal-04455951v1/file/2107.03049.pdf
Rights:
info:eu-repo/semantics/OpenAccess
Accession Number:
edsbas.1C01BD05
Database:
BASE

Weitere Informationen

11 pages, 6 figures ; 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.