# Deep Discrete Hashing with Self-supervised Pairwise Labels

arXiv: https://arxiv.org/abs/1707.02112 · HTML: https://mlsanity.com/paper/1707.02112/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Jul 2017.

## Built on by (most cited first, 8 of 8)

- [Knowledge-Aligned Counterfactual-Enhancement Diffusion Perception for Unsupervised Cross-Domain Visual Emotion Recognition](https://mlsanity.com/paper/2505.19694/) (2025)
- [Exploiting long-term temporal dynamics for video captioning](https://mlsanity.com/paper/2202.10828/) (2022)
- [Unsupervised Domain-adaptive Hash for Networks](https://mlsanity.com/paper/2108.09136/) (2021)
- [Can We Build Scene Graphs, Not Classify Them? FlowSG: Progressive Image-Conditioned Scene Graph Generation with Flow Matching](https://mlsanity.com/paper/2604.18623/) (2026)
- [CAGE-SGG: Counterfactual Active Graph Evidence for Open-Vocabulary Scene Graph Generation](https://mlsanity.com/paper/2604.22274/) (2026)
- [ReLIC-SGG: Relation Lattice Completion for Open-Vocabulary Scene Graph Generation](https://mlsanity.com/paper/2604.22546/) (2026)
- [RelWitness: Open-Vocabulary 3D Scene Graph Generation with Visual-Geometric Relation Witnesses](https://mlsanity.com/paper/2605.20823/) (2026)
- [Resolving Ambiguity in Composed Image Retrieval via Calibrated Interaction](https://mlsanity.com/paper/2605.24634/) (2026)

## Abstract (arXiv)

Hashing methods have been widely used for applications of large-scale image retrieval and classification. Non-deep hashing methods using handcrafted features have been significantly outperformed by deep hashing methods due to their better feature representation and end-to-end learning framework. However, the most striking successes in deep hashing have mostly involved discriminative models, which require labels. In this paper, we propose a novel unsupervised deep hashing method, named Deep Discrete Hashing (DDH), for large-scale image retrieval and classification. In the proposed framework, we address two main problems: 1) how to directly learn discrete binary codes? 2) how to equip the binary representation with the ability of accurate image retrieval and classification in an unsupervised way? We resolve these problems by introducing an intermediate variable and a loss function steering the learning process, which is based on the neighborhood structure in the original space. Experimental results on standard datasets (CIFAR-10, NUS-WIDE, and Oxford-17) demonstrate that our DDH significantly outperforms existing hashing methods by large margin in terms of~mAP for image retrieval and object recognition. Code is available at urlhttps://github.com/htconquer/ddh.