Unsupervised Domain Adaptation Using Full-Feature Whitening and Colouring

It is a very well known fact in computer vision that classifiers trained on source datasets do not perform well when tested on other datasets acquired under different conditions. To this end, Unsupervised Domain adaptation (UDA) methods address the shift between the source and target domain by adapting the classifier to work well in the target domain despite having no access to the target labels. A handful of UDA methods bridge domain shift by aligning the source and target feature distributions through embedded domain alignment layers that are based on batch normalization (BN) or grouped whitening. Contrarily, in this work we propose to align feature distributions with domain specific full-feature whitening and domain agnostic colouring transforms, abbreviated as \(\text {F}^{2}\text {WCT}\). The proposed \(\text {F}^{2}\text {WCT}\) optimally aligns the feature distributions by ensuring that the source and target features have identical covariance matrices. Our claim is also substantiated by the experimental results on Digits datasets for both single source and multi source unsupervised adaptation settings.