@inproceedings{6b2132d8640247e39de34b7c0c465485,
title = "DAH: Domain Adapted Deep Image Hashing",
abstract = "With abundant labeled data, deep hashing methods have shown great success in image retrieval. However, these methods are often less powerful when applied to novel datasets. In this paper, we apply unsupervised domain adaptation techniques to improve a state-of-the-art deep hashing method, used in a cross-domain scenario where the model is trained with labeled source data and is evaluated with target data. Experiments show that the generalization capability of a supervised hashing method can be improved by the applied domain adaptation techniques.",
keywords = "Batch normalization, Deep learning, Domain adaptation, Image hashing, Image retrieval",
author = "Lu, {Pei Jung} and Ma, {Pao Yun} and Chang, {Ying Ying} and Yeh, {Mei Chen}",
note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 2021 International Symposium on Intelligent Signal Processing and Communication Systems, ISPACS 2021 ; Conference date: 16-11-2021 Through 19-11-2021",
year = "2021",
doi = "10.1109/ISPACS51563.2021.9651027",
language = "English",
series = "ISPACS 2021 - International Symposium on Intelligent Signal Processing and Communication Systems: 5G Dream to Reality, Proceeding",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "ISPACS 2021 - International Symposium on Intelligent Signal Processing and Communication Systems",
}