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Accelerating Large-Scale Inference with Anisotropic Vector Quantization

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arxiv 1908.10396 v5 pith:O72FTWY7 submitted 2019-08-27 cs.LG stat.ML

Ruiqi Guo , Philip Sun , Erik Lindgren , Quan Geng , David Simcha , Felix Chern , Sanjiv Kumar This is my paper · ORCID
classification cs.LGstat.ML
keywords quantizationanisotropiccomponentdatabasefunctionsinnerlosspoints
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Quantization based techniques are the current state-of-the-art for scaling maximum inner product search to massive databases. Traditional approaches to quantization aim to minimize the reconstruction error of the database points. Based on the observation that for a given query, the database points that have the largest inner products are more relevant, we develop a family of anisotropic quantization loss functions. Under natural statistical assumptions, we show that quantization with these loss functions leads to a new variant of vector quantization that more greatly penalizes the parallel component of a datapoint's residual relative to its orthogonal component. The proposed approach achieves state-of-the-art results on the public benchmarks available at \url{ann-benchmarks.com}.

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