为向量搜索设计个性化非均匀量化，提升精度且开销极小。
Individualized non-uniform quantization for vector search
- 为每个向量单独学习非均匀量化，更精准压缩。
- 相比现有方法，在高保真场景下精度更高，计算成本几乎不变。
- 适合对检索精度要求高的大规模向量搜索系统。
嵌入向量广泛用于表示非结构化数据并搜索语义相似项。然而，由于维度高，这些向量体积庞大，导致现代向量搜索面临内存/存储读取昂贵、占用空间大的问题。本文提出NVQ（非均匀向量量化），一种在高保真条件下计算与存储效率更高的新向量压缩技术。其核心是采用新颖的简洁且计算高效的非线性函数构建非均匀量化器，且每个索引向量均独立学习对应的量化器。实验表明，NVQ在保持极低计算开销的同时，相比当前最优方法实现了更高的检索精度。
原文摘要 · Abstract (English)
Embedding vectors are widely used for representing unstructured data and searching through it for semantically similar items. However, the large size of these vectors, due to their high-dimensionality, creates problems for modern vector search techniques: retrieving large vectors from memory/storage is expensive and their footprint is costly. In this work, we present NVQ (non-uniform vector quantization), a new vector compression technique that is computationally and spatially efficient in the high-fidelity regime. The core in NVQ is to use novel parsimonious and computationally efficient nonlinearities for building non-uniform vector quantizers. Critically, these quantizers are \emph{individually} learned for each indexed vector. Our experimental results show that NVQ exhibits improved accuracy compared to the state of the art with a minimal computational cost.
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