In this work we propose a novel deep learning framework that can exploit labeled source data and unlabeled target data to learn informative hash codes to accurately classify unseen target data. To the best of our knowlege this is the first research effort to exploit the feature learning capabilities of deep neural networks to learn representative hash codes to address the domain adaptation problem.
We also introduce a new object recognition dataset Office-Home for domain adaptation.
The deep learning code is based off MatConvNet - a MATLAB toolbox implementing Convolutional Neural Networks (CNNs) for computer vision applications. Check out homepage to know more.
The paper can be cited using
@inproceedings{venkateswara2017Deep,
title={Deep Hashing Network for Unsupervised Domain Adaptation},
author={Venkateswara, Hemanth and Eusebio, Jose and Chakraborty, Shayok and Panchanathan, Sethuraman},
booktitle={IEEE} Conf. on Computer Vision and Pattern Recognition ({CVPR})},
year={2017}
}