# Asymmetric Deep Supervised Hashing

arXiv: https://arxiv.org/abs/1707.08325 · HTML: https://mlsanity.com/paper/1707.08325/

## Key facts

- Cited by 35 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Jul 2017.

## Built on by (most cited first, 30 of 35)

- [OSCAR-Net: Object-centric Scene Graph Attention for Image Attribution](https://mlsanity.com/paper/2108.03541/) (2021)
- [SEMICON: A Learning-to-hash Solution for Large-scale Fine-grained Image Retrieval](https://mlsanity.com/paper/2209.13833/) (2022)
- [HashVFL: Defending Against Data Reconstruction Attacks in Vertical Federated Learning](https://mlsanity.com/paper/2212.00325/) (2022)
- [Prototype-supervised Adversarial Network for Targeted Attack of Deep Hashing](https://mlsanity.com/paper/2105.07553/) (2021)
- [Hybrid Contrastive Quantization for Efficient Cross-View Video Retrieval](https://mlsanity.com/paper/2202.03384/) (2022)
- [Attribute-Aware Deep Hashing with Self-Consistency for Large-Scale Fine-Grained Image Retrieval](https://mlsanity.com/paper/2311.12894/) (2023)
- [VPN: Video Provenance Network for Robust Content Attribution](https://mlsanity.com/paper/2109.10038/) (2021)
- [Imitation Learning from Pixel-Level Demonstrations by HashReward](https://mlsanity.com/paper/1909.03773/) (2019)
- [Attributes Grouping and Mining Hashing for Fine-Grained Image Retrieval](https://mlsanity.com/paper/2311.06067/) (2023)
- [BiGR: Harnessing Binary Latent Codes for Image Generation and Improved Visual Representation Capabilities](https://mlsanity.com/paper/2410.14672/) (2024)
- [HASH-RAG: Bridging Deep Hashing with Retriever for Efficient, Fine Retrieval and Augmented Generation](https://mlsanity.com/paper/2505.16133/) (2025)
- [Rescuing Deep Hashing from Dead Bits Problem](https://mlsanity.com/paper/2102.00648/) (2021)
- [Deep Self-Adaptive Hashing for Image Retrieval](https://mlsanity.com/paper/2108.07094/) (2021)
- [PHPQ: Pyramid Hybrid Pooling Quantization for Efficient Fine-Grained Image Retrieval](https://mlsanity.com/paper/2109.05206/) (2021)
- [A Feature Consistency Driven Attention Erasing Network for Fine-Grained Image Retrieval](https://mlsanity.com/paper/2110.04479/) (2021)
- [Deep Asymmetric Hashing with Dual Semantic Regression and Class Structure Quantization](https://mlsanity.com/paper/2110.12478/) (2021)
- [Revisiting Medical Image Retrieval via Knowledge Consolidation](https://mlsanity.com/paper/2503.09370/) (2025)
- [Deep Hashing with Semantic Hash Centers for Image Retrieval](https://mlsanity.com/paper/2507.08404/) (2025)
- [Asymmetric Hash Code Learning for Remote Sensing Image Retrieval](https://mlsanity.com/paper/2201.05772/) (2022)
- [CgAT: Center-Guided Adversarial Training for Deep Hashing-Based Retrieval](https://mlsanity.com/paper/2204.10779/) (2022)
- [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)
- [Joint Triplet Autoencoder for Histopathological Colon Cancer Nuclei Retrieval](https://mlsanity.com/paper/2105.10262/) (2021)
- [CoopHash: Cooperative Learning of Multipurpose Descriptor and Contrastive Pair Generator via Variational MCMC Teaching for Supervised Image Hashing](https://mlsanity.com/paper/2210.04288/) (2022)
- [Cascading Hierarchical Networks with Multi-task Balanced Loss for Fine-grained hashing](https://mlsanity.com/paper/2303.11274/) (2023)
- [Learning Attribute-Aware Hash Codes for Fine-Grained Image Retrieval via Query Optimization](https://mlsanity.com/paper/2509.17049/) (2025)
- [Codebook-Centric Deep Hashing: End-to-End Joint Learning of Semantic Hash Centers and Neural Hash Function](https://mlsanity.com/paper/2511.12162/) (2025)
- [UniHash: Unifying Pointwise and Pairwise Hashing Paradigms](https://mlsanity.com/paper/2601.09828/) (2026)
- [Collision-Resistant Single-Pass Method for Unsupervised Fine-Grained Image Hashing](https://mlsanity.com/paper/2605.18288/) (2026)
- [Asymmetric Transfer Hashing with Adaptive Bipartite Graph Learning](https://mlsanity.com/paper/2206.12592/) (2022)

## Abstract (arXiv)

Hashing has been widely used for large-scale approximate nearest neighbor search because of its storage and search efficiency. Recent work has found that deep supervised hashing can significantly outperform non-deep supervised hashing in many applications. However, most existing deep supervised hashing methods adopt a symmetric strategy to learn one deep hash function for both query points and database (retrieval) points. The training of these symmetric deep supervised hashing methods is typically time-consuming, which makes them hard to effectively utilize the supervised information for cases with large-scale database. In this paper, we propose a novel deep supervised hashing method, called asymmetric deep supervised hashing (ADSH), for large-scale nearest neighbor search. ADSH treats the query points and database points in an asymmetric way. More specifically, ADSH learns a deep hash function only for query points, while the hash codes for database points are directly learned. The training of ADSH is much more efficient than that of traditional symmetric deep supervised hashing methods. Experiments show that ADSH can achieve state-of-the-art performance in real applications.