Accelerating Large-Scale Inference with Anisotropic Vector Quantization
Topics: AI (Deep Learning), Document Classification, Indexing, Ranking
This paper introduces a novel family of score-aware quantization loss functions for accelerating large-scale maximum inner product search (MIPS). Unlike traditional approaches that minimize overall reconstruction error, the proposed anisotropic loss places greater emphasis on preserving inner products that are likely to be among the top results. Under reasonable statistical assumptions, the authors show that this leads to a quantization scheme that penalizes errors along the direction of each data vector more heavily than orthogonal errors. Empirical evaluations on standard benchmarks demonstrate that anisotropic vector and product quantization significantly improve recall and inner-product estimation accuracy, achieving state-of-the-art performance.
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