  It has been well proved that deep networks are efficient at extracting
features from a given (source) labeled dataset. However, it is not always the
case that they can generalize well to other (target) datasets which very often
have a different underlying distribution. In this report, we evaluate four
different domain adaptation techniques for image classification tasks:
DeepCORAL, DeepDomainConfusion, CDAN and CDAN+E. These techniques are
unsupervised given that the target dataset dopes not carry any labels during
training phase. We evaluate model performance on the office-31 dataset. A link
to the github repository of this report can be found here:
https://github.com/agrija9/Deep-Unsupervised-Domain-Adaptation.
