TechRxiv
ell2.12317.pdf (163.31 kB)
Download file

Deep Learning to Ternary Hash Codes by Continuation

Download (163.31 kB)
preprint
posted on 06.12.2021, 19:38 authored by Mingrui Chen, Weiyu Li, weizhi luweizhi lu
Recently, it has been observed that $\{0,\pm1\}$-ternary codes which are simply generated from deep features by hard thresholding, tend to outperform $\{-1, 1\}$-binary codes in image retrieval. To obtain better ternary codes, we for the first time propose to jointly learn the features with the codes by appending a smoothed function to the networks. During training, the function could evolve into a non-smoothed ternary function by a continuation method, and then generate ternary codes. The method circumvents the difficulty of directly training discrete functions and reduces the quantization errors of ternary codes. Experiments show that the proposed joint learning indeed could produce better ternary codes.

History

Email Address of Submitting Author

wzlu@sdu.edu.cn

Submitting Author's Institution

Shandong University

Submitting Author's Country

China

Usage metrics

Read the peer-reviewed publication

in Electronics Letters

Licence

Exports