NeurIPS'23 ANN竞赛揭示了复杂场景下近似最近邻搜索的新突破。
Results of the Big ANN: NeurIPS'23 competition
- 针对过滤、分布外数据等复杂场景设计新索引与搜索算法
- 在有限资源下实现比工业基准更高的精度与效率
- 适合关注高效向量检索的科研与工程人员
2023年NeurIPS大会举办的大型近似最近邻（ANN）挑战赛，聚焦于推动实际应用场景中近似最近邻搜索（ANNS）数据结构与搜索算法的前沿进展。与以往强调经典ANN扩展性不同，本次竞赛覆盖了带过滤搜索、分布外数据、稀疏及流式数据等复杂工作负载。参赛者在新标准数据集上提交创新方案，并在受限计算资源下进行评估。结果表明，顶尖方案在搜索精度与效率上显著优于行业基准，来自学术界和产业界的团队均有突出贡献。本文总结了竞赛赛道、数据集、评估指标及优胜方案的创新方法，揭示了该领域的最新进展与未来方向。
原文摘要 · Abstract (English)
The 2023 Big ANN Challenge, held at NeurIPS 2023, focused on advancing the state-of-the-art in indexing data structures and search algorithms for practical variants of Approximate Nearest Neighbor (ANN) search that reflect the growing complexity and diversity of workloads. Unlike prior challenges that emphasized scaling up classical ANN search ~\cite{DBLP:conf/nips/SimhadriWADBBCH21}, this competition addressed filtered search, out-of-distribution data, sparse and streaming variants of ANNS. Participants developed and submitted innovative solutions that were evaluated on new standard datasets with constrained computational resources. The results showcased significant improvements in search accuracy and efficiency over industry-standard baselines, with notable contributions from both academic and industrial teams. This paper summarizes the competition tracks, datasets, evaluation metrics, and the innovative approaches of the top-performing submissions, providing insights into the current advancements and future directions in the field of approximate nearest neighbor search.
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