Awesome Domain Adaptation Package Toolbox for Tensorflow and Scikit-learn
Project description
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
cvxopt
If for some reason, these packages failed to install, you can do it manually with:
pip install numpy scipy tensorflow scikit-learn cvxopt
Finally import the module in your python scripts with:
import adapt
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)
Acknowledgement
Part of this work has been funded by the Industrial Data Analytics and Machine Learning chair from ENS Paris-Saclay, Borelli center.
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