mahfujur1 / adapt

Awesome Domain Adaptation Package Toolbox

ADAPT

Awesome Domain Adaptation Package Toolbox

ADAPT is a python library which provides several domain adaptation methods usefull to improve machine learning models.

Documentation Website

Find the details of all implemented methods as well as illustrative examples here: ADAPT Documentation Website

Installation

This package is available on Pypi and can be installed with the following command line:

pip install adaptation

The following dependencies are required and will be installed with the library:

• numpy
• scipy
• tensorflow (>= 2.0)
• scikit-learn

If for some reason, these packages failed to install, you can do it manually with:

pip install numpy scipy tensorflow scikit-learn

Finally import the module in your python scripts with:

import adapt

Reference

If you use this library in your research, please cite ADAPT using the following reference:

A. de Mathelin, ADAPT Awesome Domain Adaptation Package Toolbox, 
Website: https://antoinedemathelin.github.io/adapt/_build/html/index.html, 2020

Or in BibTeX format:

@misc{demathelin2020adapt,
title={ADAPT Awesome Domain Adaptation Package Toolbox},
author={A. de Mathelin},
url={https://antoinedemathelin.github.io/adapt/_build/html/index.html},
year={2020}
}

Content

ADAPT package is divided in three sub-modules containing the following domain adaptation methods:

Feature-based methods

• FE (Frustratingly Easy Domain Adaptation)
• mSDA (marginalized Stacked Denoising Autoencoder)
• DANN (Discriminative Adversarial Neural Network)
• ADDA (Adversarial Discriminative Domain Adaptation)
• CORAL (CORrelation ALignment)
• DeepCORAL (Deep CORrelation ALignment)

Instance-based methods

• KMM (Kernel Mean Matching)
• KLIEP (Kullback–Leibler Importance Estimation Procedure)
• TrAdaBoost (Transfer AdaBoost)
• TrAdaBoostR2 (Transfer AdaBoost for Regression)
• TwoStageTrAdaBoostR2 (Two Stage Transfer AdaBoost for Regression)

Parameter-based methods

• RegularTransferLR (Regular Transfer with Linear Regression)
• RegularTransferLC (Regular Transfer with Linear Classification)
• RegularTransferNN (Regular Transfer with Neural Network)

Examples

Examples for regression and classification DA on synthetic datasets are available here:

Classification	Regression