@inproceedings{7db724364299446cbd1487921cf9c941,
title = "A New Variational Method for Deep Supervised Semantic Image Hashing",
abstract = "We present a supervised semantic hashing method which uses a variational autoencoder to represent each database image sample as a product Bernoulli distribution. We show that the probability parameters approach extreme values during training, allowing them to be used directly as hash bits. We show how our method allows balanced bits to be directly specified, and is superior to state-of-the-art methods across four datasets.",
keywords = "hashing, image, retrieval, supervised",
author = "Furen Zhuang and Pierre Moulin",
note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020 ; Conference date: 04-05-2020 Through 08-05-2020",
year = "2020",
month = may,
doi = "10.1109/ICASSP40776.2020.9053665",
language = "English (US)",
series = "ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "4532--4536",
booktitle = "2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020 - Proceedings",
address = "United States",
}