OOD-DiskANN: Efficient and Scalable Graph ANNS for Out-of-Distribution Queries

TL;DR

This paper introduces OOD-DiskANN, a scalable graph-based ANNS algorithm that efficiently handles out-of-distribution queries by leveraging a small sample of such queries, significantly improving latency.

Contribution

The work presents a novel OOD-aware ANNS method that uses limited OOD query samples to enhance search efficiency for out-of-distribution data.

Findings

Up to 40% reduction in mean query latency for OOD queries.

Scalable graph-based index with OOD query adaptation.

Improves efficiency for both OOD and ID queries.

Abstract

State-of-the-art algorithms for Approximate Nearest Neighbor Search (ANNS) such as DiskANN, FAISS-IVF, and HNSW build data dependent indices that offer substantially better accuracy and search efficiency over data-agnostic indices by overfitting to the index data distribution. When the query data is drawn from a different distribution - e.g., when index represents image embeddings and query represents textual embeddings - such algorithms lose much of this performance advantage. On a variety of datasets, for a fixed recall target, latency is worse by an order of magnitude or more for Out-Of-Distribution (OOD) queries as compared to In-Distribution (ID) queries. The question we address in this work is whether ANNS algorithms can be made efficient for OOD queries if the index construction is given access to a small sample set of these queries. We answer positively by presenting OOD-DiskANN, which uses a sparing sample (1% of index set size) of OOD queries, and provides up to 40% improvement in mean query latency over SoTA algorithms of a similar memory footprint. OOD-DiskANN is scalable and has the efficiency of graph-based ANNS indices. Some of our contributions can improve query efficiency for ID queries as well.