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A General and Efficient Querying Method for Learning to Hash

Summary: Introduces quantization distance (QD), a fine-grained similarity indicator to replace Hamming distance in learning-to-hash for ANN. Proposes two efficient QD-based querying methods that surpass Hamming ranking and generalize across L2H algorithms, delivering noticeable gains with a simpler design. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
5470
Venue
SIGMOD
Year
2018
Pagerank
6.0528541e-05
Overall Rank
4,609 | 67.94%
DOI
10.1145/3183713.3183750

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