mlsanityData to 14 Oct 2026

Hashing as Tie-Aware Learning to Rank

arXiv 1705.08562 ↗First posted 23 May 2017stat.ML cs.CV cs.LGcited by 18

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Abstract (arXiv)

Hashing, or learning binary embeddings of data, is frequently used in nearest neighbor retrieval. In this paper, we develop learning to rank formulations for hashing, aimed at directly optimizing ranking-based evaluation metrics such as Average Precision (AP) and Normalized Discounted Cumulative Gain (NDCG). We first observe that the integer-valued Hamming distance often leads to tied rankings, and propose to use tie-aware versions of AP and NDCG to evaluate hashing for retrieval. Then, to optimize tie-aware ranking metrics, we derive their continuous relaxations, and perform gradient-based optimization with deep neural networks. Our results establish the new state-of-the-art for image retrieval by Hamming ranking in common benchmarks.

Key facts. Cited by 18 papers in the map (data through 14 Oct 2026). First posted on arXiv 23 May 2017.

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Citing papers per year: 2021: 8; 2022: 3; 2023: 4; 2024: 1; 2025: 0; 2026: 2.

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  1. 2022RankSim: Ranking Similarity Regularization for Deep Imbalanced Regressioncited by 52
  2. 2021Large-Scale Spatio-Temporal Person Re-identification: Algorithms and Benchmarkcited by 47
  3. 2021Learning Intra-Batch Connections for Deep Metric Learningcited by 34
  4. 2021Robust and Decomposable Average Precision for Image Retrievalcited by 19
  5. 2023Optimization of Rank Losses for Image Retrievalcited by 10
  6. 2021MetricOpt: Learning to Optimize Black-Box Evaluation Metricscited by 9
  7. 2021TransHash: Transformer-based Hamming Hashing for Efficient Image Retrievalcited by 8
  8. 2022Exploring the Algorithm-Dependent Generalization of AUPRC Optimization with List Stabilitycited by 8
  9. 2023Attribute-Aware Deep Hashing with Self-Consistency for Large-Scale Fine-Grained Image Retrievalcited by 8
  10. 2022The Group Loss++: A deeper look into group loss for deep metric learningcited by 6
  11. 2021Rethinking the Optimization of Average Precision: Only Penalizing Negative Instances before Positive Ones is Enoughcited by 4
  12. 2023DeepLSH: Deep Locality-Sensitive Hash Learning for Fast and Efficient Near-Duplicate Crash Report Detectioncited by 3
6 more
  1. 2024Unlocking the Potential: Multi-task Deep Learning for Spaceborne Quantitative Monitoring of Fugitive Methane Plumescited by 3
  2. 2021Optimizing Area Under the Curve Measures via Matrix Factorization for Predicting Drug-Target Interaction with Multiple Similaritiescited by 2
  3. 2023Ranking Regularization for Critical Rare Classes: Minimizing False Positives at a High True Positive Ratecited by 1
  4. 2026Pointing the Way, Hiding the Destination: Practical Private Dense Retrieval at Scalecited by 1
  5. 2021Unsupervised Embedding Learning from Uncertainty Momentum Modelingcited by 0
  6. 2026DSCH-Loss: A Dynamic Semantic Channel Objective for Deep Semantic Hashingcited by 0