OOD-DiskANN: Efficient and Scalable Graph ANNS for Out-of-Distribution Queries. (arXiv:2211.12850v1 [cs.LG])

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 …

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