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Deep Learning to Ternary Hash Codes by Continuation
  • Mingrui Chen ,
  • Weiyu Li ,
  • weizhi lu
Mingrui Chen
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weizhi lu
Shandong University

Corresponding Author:[email protected]

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Abstract

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.
Nov 2021Published in Electronics Letters volume 57 issue 24 on pages 925-926. 10.1049/ell2.12317