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.
@article{arxiv.2109.13420,
title = {Evaluation of Deep Neural Network Domain Adaptation Techniques for Image Recognition},
author = {Alan Preciado-Grijalva and Venkata Santosh Sai Ramireddy Muthireddy},
journal= {arXiv preprint arXiv:2109.13420},
year = {2021}
}