# Ternary Hashing

arXiv: https://arxiv.org/abs/2103.09173 · HTML: https://mlsanity.com/paper/2103.09173/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 30 references that are in the map.
- First posted on arXiv 16 Mar 2021.

## Builds on (most cited first, 11 of 11)

- [ImageNet Large Scale Visual Recognition Challenge](https://mlsanity.com/paper/1409.0575/) (2014)
- [TernGrad: Ternary Gradients to Reduce Communication in Distributed Deep Learning](https://mlsanity.com/paper/1705.07878/) (2017)
- [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)
- [Feature Learning based Deep Supervised Hashing with Pairwise Labels](https://mlsanity.com/paper/1511.03855/) (2015)
- [A Survey on Deep Hashing Methods](https://mlsanity.com/paper/2003.03369/) (2020)
- [Ternary Neural Networks for Resource-Efficient AI Applications](https://mlsanity.com/paper/1609.00222/) (2016)
- [Hashing with Mutual Information](https://mlsanity.com/paper/1803.00974/) (2018)
- [Unrolling Ternary Neural Networks](https://mlsanity.com/paper/1909.04509/) (2019)
- [Embarrassingly Simple Binary Representation Learning](https://mlsanity.com/paper/1908.09573/) (2019)
- [Neural Networks Weights Quantization: Target None-retraining Ternary (TNT)](https://mlsanity.com/paper/1912.09236/) (2019)

## Built on by (most cited first, 2 of 2)

- [Deep Learning to Ternary Hash Codes by Continuation](https://mlsanity.com/paper/2107.07987/) (2021)
- [Ternary and Binary Quantization for Improved Classification](https://mlsanity.com/paper/2203.16798/) (2022)

## Abstract (arXiv)

This paper proposes a novel ternary hash encoding for learning to hash methods, which provides a principled more efficient coding scheme with performances better than those of the state-of-the-art binary hashing counterparts. Two kinds of axiomatic ternary logic, Kleene logic and Lukasiewicz logic are adopted to calculate the Ternary Hamming Distance (THD) for both the learning/encoding and testing/querying phases. Our work demonstrates that, with an efficient implementation of ternary logic on standard binary machines, the proposed ternary hashing is compared favorably to the binary hashing methods with consistent improvements of retrieval mean average precision (mAP) ranging from 1% to 5.9% as shown in CIFAR10, NUS-WIDE and ImageNet100 datasets.