# Deep Supervised Discrete Hashing

arXiv: https://arxiv.org/abs/1705.10999 · HTML: https://mlsanity.com/paper/1705.10999/

## Key facts

- Cited by 36 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 May 2017.

## Built on by (most cited first, 30 of 36)

- [One Loss for All: Deep Hashing with a Single Cosine Similarity based Learning Objective](https://mlsanity.com/paper/2109.14449/) (2021)
- [Self-supervised Product Quantization for Deep Unsupervised Image Retrieval](https://mlsanity.com/paper/2109.02244/) (2021)
- [Hash3D: Training-free Acceleration for 3D Generation](https://mlsanity.com/paper/2404.06091/) (2024)
- [OSCAR-Net: Object-centric Scene Graph Attention for Image Attribution](https://mlsanity.com/paper/2108.03541/) (2021)
- [One Loss for Quantization: Deep Hashing with Discrete Wasserstein Distributional Matching](https://mlsanity.com/paper/2205.15721/) (2022)
- [VPN: Video Provenance Network for Robust Content Attribution](https://mlsanity.com/paper/2109.10038/) (2021)
- [OTB-morph: One-Time Biometrics via Morphing](https://mlsanity.com/paper/2302.09053/) (2023)
- [GREN: Graph-Regularized Embedding Network for Weakly-Supervised Disease Localization in X-ray Images](https://mlsanity.com/paper/2107.06442/) (2021)
- [Deep Self-Adaptive Hashing for Image Retrieval](https://mlsanity.com/paper/2108.07094/) (2021)
- [DeepLSH: Deep Locality-Sensitive Hash Learning for Fast and Efficient Near-Duplicate Crash Report Detection](https://mlsanity.com/paper/2310.06703/) (2023)
- [Learning Position-Aware Implicit Neural Network for Real-World Face Inpainting](https://mlsanity.com/paper/2401.10537/) (2024)
- [Rank-Consistency Deep Hashing for Scalable Multi-Label Image Search](https://mlsanity.com/paper/2102.01486/) (2021)
- [Improved Deep Classwise Hashing With Centers Similarity Learning for Image Retrieval](https://mlsanity.com/paper/2103.09442/) (2021)
- [DVHN: A Deep Hashing Framework for Large-scale Vehicle Re-identification](https://mlsanity.com/paper/2112.04937/) (2021)
- [CgAT: Center-Guided Adversarial Training for Deep Hashing-Based Retrieval](https://mlsanity.com/paper/2204.10779/) (2022)
- [A Lower Bound of Hash Codes' Performance](https://mlsanity.com/paper/2210.05899/) (2022)
- [Deep Hashing via Householder Quantization](https://mlsanity.com/paper/2311.04207/) (2023)
- [Joint Triplet Autoencoder for Histopathological Colon Cancer Nuclei Retrieval](https://mlsanity.com/paper/2105.10262/) (2021)
- [Similarity Embedding Networks for Robust Human Activity Recognition](https://mlsanity.com/paper/2106.15283/) (2021)
- [Orthonormal Product Quantization Network for Scalable Face Image Retrieval](https://mlsanity.com/paper/2107.00327/) (2021)
- [Similarity Guided Deep Face Image Retrieval](https://mlsanity.com/paper/2107.05025/) (2021)
- [SImProv: Scalable Image Provenance Framework for Robust Content Attribution](https://mlsanity.com/paper/2206.14245/) (2022)
- [Set2Box: Similarity Preserving Representation Learning of Sets](https://mlsanity.com/paper/2210.03282/) (2022)
- [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)
- [Leveraging High-Resolution Features for Improved Deep Hashing-based Image Retrieval](https://mlsanity.com/paper/2403.13747/) (2024)
- [Codebook-Centric Deep Hashing: End-to-End Joint Learning of Semantic Hash Centers and Neural Hash Function](https://mlsanity.com/paper/2511.12162/) (2025)
- [Hard Example Guided Hashing for Image Retrieval](https://mlsanity.com/paper/2112.13565/) (2021)
- [Self-Supervised Consistent Quantization for Fully Unsupervised Image Retrieval](https://mlsanity.com/paper/2206.09806/) (2022)
- [Cross-Scale Context Extracted Hashing for Fine-Grained Image Binary Encoding](https://mlsanity.com/paper/2210.07572/) (2022)
- [Reliable and Efficient Evaluation of Adversarial Robustness for Deep Hashing-Based Retrieval](https://mlsanity.com/paper/2303.12658/) (2023)

## Abstract (arXiv)

With the rapid growth of image and video data on the web, hashing has been extensively studied for image or video search in recent years. Benefit from recent advances in deep learning, deep hashing methods have achieved promising results for image retrieval. However, there are some limitations of previous deep hashing methods (e.g., the semantic information is not fully exploited). In this paper, we develop a deep supervised discrete hashing algorithm based on the assumption that the learned binary codes should be ideal for classification. Both the pairwise label information and the classification information are used to learn the hash codes within one stream framework. We constrain the outputs of the last layer to be binary codes directly, which is rarely investigated in deep hashing algorithm. Because of the discrete nature of hash codes, an alternating minimization method is used to optimize the objective function. Experimental results have shown that our method outperforms current state-of-the-art methods on benchmark datasets.