SOAR: Improved Indexing for Approximate Nearest Neighbor Search

Philip Sun, David Simcha, Dave Dopson, Ruiqi Guo, Sanjiv Kumar

Keywords: ann, quantization, mips, nearest neighbor search, retrieval

Abstract: This paper introduces SOAR: **S**pilling with **O**rthogonality-**A**mplified **R**esiduals, a novel data indexing technique for approximate nearest neighbor (ANN) search. SOAR extends upon previous approaches to ANN search, such as spill trees, that utilize multiple redundant representations while partitioning the data to reduce the probability of missing a nearest neighbor during search. Rather than training and computing these redundant representations independently, however, SOAR uses an *orthogonality-amplified residual* loss, which optimizes each representation to compensate for cases where other representations perform poorly. This drastically improves the overall index quality, resulting in state-of-the-art ANN benchmark performance while maintaining fast indexing times and low memory consumption.

Submission Number: 3379