L2hforadaptivity Ef F1 F3 F5 ~upd~ Jun 2026

L2H (Learning to Hash) is a technique used for efficient similarity search and clustering in high-dimensional data. Adaptivity is a crucial aspect of L2H, as it enables the algorithm to adjust to changing data distributions and improve its performance over time. In this report, we focus on three families of L2H functions: F1, F3, and F5. We provide a detailed analysis of their performance, adaptivity, and applications.

with, e.g., α=1, β=1, γ=0.5 to emphasize gradient errors. Marking uses the (mark top % of elements by η_K). l2hforadaptivity ef f1 f3 f5