Approximate Nearest Neighbor Search

Approximate Nearest Neighbor Search (ANNS) aims to efficiently find data points closest to a given query in high-dimensional spaces, a crucial task in many machine learning applications. Current research emphasizes improving the speed and accuracy of ANNS, particularly for large-scale datasets and diverse query distributions (including out-of-distribution queries), focusing on graph-based, tree-based, hashing-based, and quantization-based methods, often incorporating parallel processing and hardware optimizations. These advancements are vital for improving the performance of various applications, including recommendation systems, information retrieval, and visual localization, by enabling faster and more accurate similarity searches on massive datasets. The field is also actively developing benchmarks and standardized evaluation metrics to facilitate comparison and progress.