# Deep Learning to Ternary Hash Codes by Continuation

arXiv: https://arxiv.org/abs/2107.07987 · HTML: https://mlsanity.com/paper/2107.07987/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 19 references that are in the map.
- First posted on arXiv 16 Jul 2021.

## Builds on (most cited first, 4 of 4)

- [HashNet: Deep Learning to Hash by Continuation](https://mlsanity.com/paper/1702.00758/) (2017)
- [A Survey on Learning to Hash](https://mlsanity.com/paper/1606.00185/) (2016)
- [Simultaneous Feature Learning and Hash Coding with Deep Neural Networks](https://mlsanity.com/paper/1504.03410/) (2015)
- [Ternary Hashing](https://mlsanity.com/paper/2103.09173/) (2021)

## Built on by (most cited first, 1 of 1)

- [Ternary and Binary Quantization for Improved Classification](https://mlsanity.com/paper/2203.16798/) (2022)

## Abstract (arXiv)

Recently, it has been observed that {0,1,-1}-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. The method circumvents the difficulty of directly training discrete functions and reduces the quantization errors of ternary codes. Experiments show that the generated codes indeed could achieve higher retrieval accuracy.