mlsanityData to 7 Oct 2026

Ternary Hashing

arXiv 2103.09173 ↗First posted 16 Mar 2021cs.AIcited by 2

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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.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 11 of its 30 references that are in the map. First posted on arXiv 16 Mar 2021.

Builds on

11 of 30 references.

References · 11

  1. 2014ImageNet Large Scale Visual Recognition Challengecited by 8,441
  2. 2017TernGrad: Ternary Gradients to Reduce Communication in Distributed Deep Learningcited by 253
  3. 2017HashNet: Deep Learning to Hash by Continuationcited by 121
  4. 2016A Survey on Learning to Hashcited by 119
  5. 2015Feature Learning based Deep Supervised Hashing with Pairwise Labelscited by 59
  6. 2020A Survey on Deep Hashing Methodscited by 47
  7. 2016Ternary Neural Networks for Resource-Efficient AI Applicationscited by 30
  8. 2018Hashing with Mutual Informationcited by 9
  9. 2019Unrolling Ternary Neural Networkscited by 7
  10. 2019Embarrassingly Simple Binary Representation Learningcited by 3
  11. 2019Neural Networks Weights Quantization: Target None-retraining Ternary (TNT)cited by 3

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Cited by year

Citing papers per year: 2021: 1; 2022: 1.

2 papers.

  1. 2021Deep Learning to Ternary Hash Codes by Continuationcited by 1
  2. 2022Ternary and Binary Quantization for Improved Classificationcited by 0