General And Practical Tuning Method For Off-the-shelf Graph-based Index: SISAP Indexing Challenge Report By Team Utokyo | Awesome Learning to Hash Add your paper to Learning2Hash

Awesome Learning to Hash

A Webpage dedicated to the latest research on Hash Function Learning. Maintained by Sean Moran.

Stay Updated

Contact Sean Moran about this survey or website. Made with Jekyll and Hyde.

General And Practical Tuning Method For Off-the-shelf Graph-based Index: SISAP Indexing Challenge Report By Team Utokyo

Yutaro Oguri, Yusuke Matsui . Lecture Notes in Computer Science 2023 – 3 citations

[Paper]   Search on Google Scholar   Search on Semantic Scholar
Evaluation Graph Based ANN

Despite the efficacy of graph-based algorithms for Approximate Nearest Neighbor (ANN) searches, the optimal tuning of such systems remains unclear. This study introduces a method to tune the performance of off-the-shelf graph-based indexes, focusing on the dimension of vectors, database size, and entry points of graph traversal. We utilize a black-box optimization algorithm to perform integrated tuning to meet the required levels of recall and Queries Per Second (QPS). We applied our approach to Task A of the SISAP 2023 Indexing Challenge and got second place in the 10M and 30M tracks. It improves performance substantially compared to brute force methods. This research offers a universally applicable tuning method for graph-based indexes, extending beyond the specific conditions of the competition to broader uses.

Similar Work