Unsupervised Domain Adaptation using Feature-Whitening and Consensus Loss

Understand

A classifier trained on a dataset seldom works on other datasets obtained under different conditions due to domain shift.

• This problem is commonly addressed by domain adaptation methods.
• In this work we introduce a novel deep learning framework which unifies different paradigms in unsupervised domain adaptation.
• Specifically, we propose domain alignment layers which implement feature whitening for the purpose of matching source and target feature distributions.