The top-performing systems for billion-scale high-dimensional approximate
nearest neighbor (ANN) search are all based on two-layer architectures that
include an indexing structure and a compressed datapoints layer. An indexing
structure is crucial as it allows to avoid exhaustive search, while the lossy
data compression is needed to fit the dataset into RAM. Several of the most
successful systems use product quantization (PQ) for both the indexing and the
dataset compression layers. These systems are however limited in the way they
exploit the interaction of product quantization processes that happen at
different stages of these systems.
Here we introduce and evaluate two approximate nearest neighbor search
systems that both exploit the synergy of product quantization processes in a
more efficient way. The first system, called Fast Bilayer Product Quantization
(FBPQ), speeds up the runtime of the baseline system (Multi-D-ADC) by several
times, while achieving the same accuracy. The second system, Hierarchical
Bilayer Product Quantization (HBPQ) provides a significantly better recall for
the same runtime at a cost of small memory footprint increase. For the BIGANN
dataset of billion SIFT descriptors, the 10% increase in Recall@1 and the 17%
increase in Recall@10 is observed.

本研究针对高维数据，提出了两种基于产品量化方法的最近邻搜索系统，并通过实验证明这些系统在检索效率与精度方面优于现有的系统。