Abstract. This work addresses the problem of billion-scale nearest neighbor search. The state-of-the-art retrieval systems for billion-scale databases
are currently based on the inverted multi-index, the recently proposed
generalization of the inverted index structure. The multi-index provides
a very fine-grained partition of the feature space that allows extracting
concise and accurate short-lists of candidates for the search queries.
In this paper, we argue that the potential of the simple inverted index
was not fully exploited in previous works and advocate its usage both for
the highly-entangled deep descriptors and relatively disentangled SIFT
descriptors. We introduce a new retrieval system that is based on the
inverted index and outperforms the multi-index by a large margin for the
same memory consumption and construction complexity. For example,
our system achieves the state-of-the-art recall rates several times faster
on the dataset of one billion deep descriptors compared to the efficient
implementation of the inverted multi-index from the FAISS library