Domain adaptation is a subset of transfer learning aimed at addressing the problem of domain shift, where the distribution of the source domain differs from that of the target domain. This report delves into various techniques and methodologies employed to mitigate this issue, with a focus on adapting deep learning models to new domains effectively.

We tested the MNIST,SVHN and USPS datasets using algorithms like DANN, deep CORAL, source-only techniques.