ADAPT : Awesome Domain Adaptation Python Toolbox
- URL: http://arxiv.org/abs/2107.03049v1
- Date: Wed, 7 Jul 2021 07:20:21 GMT
- Title: ADAPT : Awesome Domain Adaptation Python Toolbox
- Authors: Antoine de Mathelin, Fran\c{c}ois Deheeger, Guillaume Richard,
Mathilde Mougeot, Nicolas Vayatis
- Abstract summary: 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.
- Score: 5.932280316886339
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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 https://adapt-python.github.io/adapt/ with gallery of examples. Besides,
the library presents an high test coverage.
Related papers
- $\texttt{skwdro}$: a library for Wasserstein distributionally robust machine learning [6.940992962425166]
skwdro is a Python library for training robust machine learning models.
It features both scikit-learn compatible estimators for popular objectives, as well as a wrapper for PyTorch modules.
arXiv Detail & Related papers (2024-10-28T17:16:00Z) - pyvene: A Library for Understanding and Improving PyTorch Models via
Interventions [79.72930339711478]
$textbfpyvene$ is an open-source library that supports customizable interventions on a range of different PyTorch modules.
We show how $textbfpyvene$ provides a unified framework for performing interventions on neural models and sharing the intervened upon models with others.
arXiv Detail & Related papers (2024-03-12T16:46:54Z) - Mixture-Models: a one-stop Python Library for Model-based Clustering
using various Mixture Models [4.60168321737677]
textttMixture-Models is an open-source Python library for fitting Gaussian Mixture Models (GMM) and their variants.
It streamlines the implementation and analysis of these models using various first/second order optimization routines.
The library provides user-friendly model evaluation tools, such as BIC, AIC, and log-likelihood estimation.
arXiv Detail & Related papers (2024-02-08T19:34:24Z) - DeeProb-kit: a Python Library for Deep Probabilistic Modelling [0.0]
DeeProb-kit is a unified library written in Python consisting of a collection of deep probabilistic models (DPMs)
It includes efficiently implemented learning techniques, inference routines, statistical algorithms, and provides high-quality fully-documented APIs.
arXiv Detail & Related papers (2022-12-08T17:02:16Z) - Solo-learn: A Library of Self-supervised Methods for Visual
Representation Learning [83.02597612195966]
solo-learn is a library of self-supervised methods for visual representation learning.
Implemented in Python, using Pytorch and Pytorch lightning, the library fits both research and industry needs.
arXiv Detail & Related papers (2021-08-03T22:19:55Z) - BOML: A Modularized Bilevel Optimization Library in Python for Meta
Learning [52.90643948602659]
BOML is a modularized optimization library that unifies several meta-learning algorithms into a common bilevel optimization framework.
It provides a hierarchical optimization pipeline together with a variety of iteration modules, which can be used to solve the mainstream categories of meta-learning methods.
arXiv Detail & Related papers (2020-09-28T14:21:55Z) - Captum: A unified and generic model interpretability library for PyTorch [49.72749684393332]
We introduce a novel, unified, open-source model interpretability library for PyTorch.
The library contains generic implementations of a number of gradient and perturbation-based attribution algorithms.
It can be used for both classification and non-classification models.
arXiv Detail & Related papers (2020-09-16T18:57:57Z) - Picasso: A Sparse Learning Library for High Dimensional Data Analysis in
R and Python [77.33905890197269]
We describe a new library which implements a unified pathwise coordinate optimization for a variety of sparse learning problems.
The library is coded in R++ and has user-friendly sparse experiments.
arXiv Detail & Related papers (2020-06-27T02:39:24Z) - Kernel methods library for pattern analysis and machine learning in
python [0.0]
The kernelmethods library fills that important void in the python ML ecosystem in a domain-agnostic fashion.
The library provides a number of well-defined classes to make various kernel-based operations efficient.
arXiv Detail & Related papers (2020-05-27T16:44:42Z) - MOGPTK: The Multi-Output Gaussian Process Toolkit [71.08576457371433]
We present MOGPTK, a Python package for multi-channel data modelling using Gaussian processes (GP)
The aim of this toolkit is to make multi-output GP (MOGP) models accessible to researchers, data scientists, and practitioners alike.
arXiv Detail & Related papers (2020-02-09T23:34:49Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.