Weighting Adversarial Neural Network (paper link: https://arxiv.org/pdf/2006.08251.pdf)
WANN is a supervised domain adaptation method suited for regression tasks. The algorithm is an instance-based method which learns a reweighting of source instance losses in order to correct the difference between source and target distributions.
Code for the numerical experiments requires the following packages:
tensorflow(>= 2.0)scikit-learnnumpypandasmatplotlibnltkadapt
WANN algorithm is compared to several domain adaptation base-lines:
- KMM Huang et al.
- KLIEP Sugiyama et al.
- TrAdaBoostR2 Pardoe et al.
- DANN Ganin et al.
- ADDA Tzeng et al.
- MDD Zhang et al.
The implementation of WANN can be found in the wann\methods folder. The implementation of the base-lines come from the ADAPT library
The experiments are conducted on one synthetic and two benchmark datasets: