학술논문
A New Variational Method for Deep Supervised Semantic Image Hashing
Document Type
Conference
Author
Source
ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) Acoustics, Speech and Signal Processing (ICASSP), ICASSP 2020 - 2020 IEEE International Conference on. :4532-4536 May, 2020
Subject
Language
ISSN
2379-190X
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.