Feature-based methods are based on the research of common features which have similar behaviour with respect to the task on source and target domain.

Algorithms: FA, CORAL, DANN, ...

The general principle of instance-based methods is to reweight labeled training data in order to correct the difference between source and target distributions.

Algorithms: KMM, KLIEP, TrAdaBoost, ...

In parameter-based methods, the parameters of one or few pre-trained models built with the source data are adapted to build a suited model for the task on the target domain.

Algorithms: RegularTransferLR, RegularTransferNN, ...

Here are some examples of domain adaptation methods applied on a classification task. Please look at these examples to learn how to use the algorithms provided by the ADAPT package.

Here are some examples of domain adaptation methods applied on a regression task. Please look at these examples to learn how to use the algorithms provided by the ADAPT package.

Here are some examples of domain adaptation methods applied on the Two Moons dataset. Please look at these examples to learn how to use the algorithms provided by the ADAPT package.

Here are some examples of domain adaptation methods applied to sample bias correction on the "Diabetes" dataset. Please look at these examples to learn how to use the algorithms provided by the ADAPT package.

Here are some examples of domain adaptation methods applied to fine-tuning on the "Flowers" dataset. Please look at these examples to learn how to use the algorithms provided by the ADAPT package.

Here are some examples of deep domain adaptation methods applied on the "Office" dataset. Please look at these examples to learn how to use the algorithms provided by the ADAPT package.