mlsanityData to 14 Oct 2026

Deep Supervised Discrete Hashing

arXiv 1705.10999 ↗First posted 31 May 2017cs.CVcited by 36

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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.

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

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Citing papers per year: 2021: 14; 2022: 8; 2023: 6; 2024: 4; 2025: 4.

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  1. 2021One Loss for All: Deep Hashing with a Single Cosine Similarity based Learning Objectivecited by 43
  2. 2021Self-supervised Product Quantization for Deep Unsupervised Image Retrievalcited by 28
  3. 2024Hash3D: Training-free Acceleration for 3D Generationcited by 19
  4. 2021OSCAR-Net: Object-centric Scene Graph Attention for Image Attributioncited by 15
  5. 2022One Loss for Quantization: Deep Hashing with Discrete Wasserstein Distributional Matchingcited by 9
  6. 2021VPN: Video Provenance Network for Robust Content Attributioncited by 7
  7. 2023OTB-morph: One-Time Biometrics via Morphingcited by 5
  8. 2021GREN: Graph-Regularized Embedding Network for Weakly-Supervised Disease Localization in X-ray Imagescited by 3
  9. 2021Deep Self-Adaptive Hashing for Image Retrievalcited by 3
  10. 2023DeepLSH: Deep Locality-Sensitive Hash Learning for Fast and Efficient Near-Duplicate Crash Report Detectioncited by 3
  11. 2024Learning Position-Aware Implicit Neural Network for Real-World Face Inpaintingcited by 3
  12. 2021Rank-Consistency Deep Hashing for Scalable Multi-Label Image Searchcited by 2
24 more
  1. 2021Improved Deep Classwise Hashing With Centers Similarity Learning for Image Retrievalcited by 2
  2. 2021DVHN: A Deep Hashing Framework for Large-scale Vehicle Re-identificationcited by 2
  3. 2022CgAT: Center-Guided Adversarial Training for Deep Hashing-Based Retrievalcited by 2
  4. 2022A Lower Bound of Hash Codes' Performancecited by 2
  5. 2023Deep Hashing via Householder Quantizationcited by 2
  6. 2021Joint Triplet Autoencoder for Histopathological Colon Cancer Nuclei Retrievalcited by 1
  7. 2021Similarity Embedding Networks for Robust Human Activity Recognitioncited by 1
  8. 2021Orthonormal Product Quantization Network for Scalable Face Image Retrievalcited by 1
  9. 2021Similarity Guided Deep Face Image Retrievalcited by 1
  10. 2022SImProv: Scalable Image Provenance Framework for Robust Content Attributioncited by 1
  11. 2022Set2Box: Similarity Preserving Representation Learning of Setscited by 1
  12. 2022CoopHash: Cooperative Learning of Multipurpose Descriptor and Contrastive Pair Generator via Variational MCMC Teaching for Supervised Image Hashingcited by 1
  13. 2024Leveraging High-Resolution Features for Improved Deep Hashing-based Image Retrievalcited by 1
  14. 2025Codebook-Centric Deep Hashing: End-to-End Joint Learning of Semantic Hash Centers and Neural Hash Functioncited by 1
  15. 2021Hard Example Guided Hashing for Image Retrievalcited by 0
  16. 2022Self-Supervised Consistent Quantization for Fully Unsupervised Image Retrievalcited by 0
  17. 2022Cross-Scale Context Extracted Hashing for Fine-Grained Image Binary Encodingcited by 0
  18. 2023Reliable and Efficient Evaluation of Adversarial Robustness for Deep Hashing-Based Retrievalcited by 0
  19. 2023Towards Efficient Deep Hashing Retrieval: Condensing Your Data via Feature-Embedding Matchingcited by 0
  20. 2023Supervised Auto-Encoding Twin-Bottleneck Hashingcited by 0
  21. 2024Distilling Vision-Language Pretraining for Efficient Cross-Modal Retrievalcited by 0
  22. 2025Adaptive von Mises-Fisher Likelihood Loss for Supervised Deep Time Series Hashingcited by 0
  23. 2025DualHash: A Stochastic Primal-Dual Algorithm with Theoretical Guarantee for Deep Hashingcited by 0
  24. 2025Model Inversion Attack Against Deep Hashingcited by 0