# A Survey on Deep Hashing Methods

arXiv: https://arxiv.org/abs/2003.03369 · HTML: https://mlsanity.com/paper/2003.03369/

## Key facts

- Cited by 47 papers in the map (data through 7 Oct 2026).
- First posted on arXiv 4 Mar 2020.

## Built on by (most cited first, 30 of 47)

- [A Comprehensive Survey on Vector Database: Storage and Retrieval Technique, Challenge](https://mlsanity.com/paper/2310.11703/) (2023)
- [Visual Tuning](https://mlsanity.com/paper/2305.06061/) (2023)
- [A Comprehensive Survey on Trustworthy Recommender Systems](https://mlsanity.com/paper/2209.10117/) (2022)
- [Accelerating Code Search with Deep Hashing and Code Classification](https://mlsanity.com/paper/2203.15287/) (2022)
- [LLC: Accurate, Multi-purpose Learnt Low-dimensional Binary Codes](https://mlsanity.com/paper/2106.01487/) (2021)
- [Content-Based Medical Image Retrieval with Opponent Class Adaptive Margin Loss](https://mlsanity.com/paper/2211.15371/) (2022)
- [Instance-weighted Central Similarity for Multi-label Image Retrieval](https://mlsanity.com/paper/2108.05274/) (2021)
- [DarkHash: A Data-Free Backdoor Attack Against Deep Hashing](https://mlsanity.com/paper/2510.08094/) (2025)
- [Unsupervised Hashing with Semantic Concept Mining](https://mlsanity.com/paper/2209.11475/) (2022)
- [Semantic-Aware Adversarial Training for Reliable Deep Hashing Retrieval](https://mlsanity.com/paper/2310.14637/) (2023)
- [LeanVec: Searching vectors faster by making them fit](https://mlsanity.com/paper/2312.16335/) (2023)
- [Unsupervised Representation Learning via Neural Activation Coding](https://mlsanity.com/paper/2112.04014/) (2021)
- [DeepLSH: Deep Locality-Sensitive Hash Learning for Fast and Efficient Near-Duplicate Crash Report Detection](https://mlsanity.com/paper/2310.06703/) (2023)
- [Deep Hashing with Semantic Hash Centers for Image Retrieval](https://mlsanity.com/paper/2507.08404/) (2025)
- [Ternary Hashing](https://mlsanity.com/paper/2103.09173/) (2021)
- [PP-ShiTu: A Practical Lightweight Image Recognition System](https://mlsanity.com/paper/2111.00775/) (2021)
- [A Lower Bound of Hash Codes' Performance](https://mlsanity.com/paper/2210.05899/) (2022)
- [Unsupervised Hashing with Similarity Distribution Calibration](https://mlsanity.com/paper/2302.07669/) (2023)
- [Learning Label Encodings for Deep Regression](https://mlsanity.com/paper/2303.02273/) (2023)
- [Deep Hashing via Householder Quantization](https://mlsanity.com/paper/2311.04207/) (2023)
- [Nested Hash Layer: A Plug-and-play Module for Multiple-length Hash Code Learning](https://mlsanity.com/paper/2412.08922/) (2024)
- [Image Hashing via Cross-View Code Alignment in the Age of Foundation Models](https://mlsanity.com/paper/2510.27584/) (2025)
- [Self-Supervised Learning as Discrete Communication](https://mlsanity.com/paper/2602.09764/) (2026)
- [Leveraging High-Resolution Features for Improved Deep Hashing-based Image Retrieval](https://mlsanity.com/paper/2403.13747/) (2024)
- [ConceptHash: Interpretable Fine-Grained Hashing via Concept Discovery](https://mlsanity.com/paper/2406.08457/) (2024)
- [Hashing-Baseline: Rethinking Hashing in the Age of Pretrained Models](https://mlsanity.com/paper/2509.14427/) (2025)
- [GPU-Accelerated Algorithms for Graph Vector Search: Taxonomy, Empirical Study, and Research Directions](https://mlsanity.com/paper/2602.16719/) (2026)
- [From Image Hashing to Scene Change Detection](https://mlsanity.com/paper/2605.12259/) (2026)
- [Collision-Resistant Single-Pass Method for Unsupervised Fine-Grained Image Hashing](https://mlsanity.com/paper/2605.18288/) (2026)
- [Pointing the Way, Hiding the Destination: Practical Private Dense Retrieval at Scale](https://mlsanity.com/paper/2608.25735/) (2026)

## Abstract (arXiv)

Nearest neighbor search aims to obtain the samples in the database with the smallest distances from them to the queries, which is a basic task in a range of fields, including computer vision and data mining. Hashing is one of the most widely used methods for its computational and storage efficiency. With the development of deep learning, deep hashing methods show more advantages than traditional methods. In this survey, we detailedly investigate current deep hashing algorithms including deep supervised hashing and deep unsupervised hashing. Specifically, we categorize deep supervised hashing methods into pairwise methods, ranking-based methods, pointwise methods as well as quantization according to how measuring the similarities of the learned hash codes. Moreover, deep unsupervised hashing is categorized into similarity reconstruction-based methods, pseudo-label-based methods and prediction-free self-supervised learning-based methods based on their semantic learning manners. We also introduce three related important topics including semi-supervised deep hashing, domain adaption deep hashing and multi-modal deep hashing. Meanwhile, we present some commonly used public datasets and the scheme to measure the performance of deep hashing algorithms. Finally, we discuss some potential research directions in conclusion.