recall-speed comparison
A supplementary for the paper Falconn++: A Locality-sensitive Filtering Approach for Approximate Nearest Neighbor Search
For ScaNN, we use the latest version 1.2.6 released on 29 April, 2022. FAISS and coCEOs do though their thread-scaling is not perfect. Table 1: Hnsw takes 13.7 mins to build 5.4GB indexing space. Based on the size of HNSW's index, we tune the number Since the characteristics of the data sets are different, it uses different values of iProbes . We used the suggested parameter provided in ScaNN's GitHub.
Technology:
Falconn++: A Locality-sensitive Filtering Approach for Approximate Nearest Neighbor Search
Falconn++ is also competitive with HNSW, an efficient representative of graph-based solutions on high search recall regimes.
Country:
- Oceania > New Zealand > North Island > Auckland Region > Auckland (0.04)
- Asia > Afghanistan > Parwan Province > Charikar (0.04)
Technology:
- Information Technology > Artificial Intelligence > Natural Language > Information Retrieval (0.64)
- Information Technology > Artificial Intelligence > Machine Learning > Statistical Learning (0.46)
- Information Technology > Artificial Intelligence > Representation & Reasoning > Case-Based Reasoning (0.42)