Vector quantization is a fundamental operation for data compression and vector search. To obtain high accuracy, multi-codebook methods increase the rate by representing each vector using codewords across multiple codebooks. Residual quantization (RQ) is one such method, which increases accuracy by iteratively quantizing the error of the previous step. The error distribution is dependent on previously selected codewords. This dependency is, however, not accounted for in conventional RQ as it uses a generic codebook per quantization step. In this paper, we propose QINCo, a neural RQ variant which predicts specialized codebooks per vector using a neural network that is conditioned on the approximation of the vector from previous steps. Experiments show that QINCo outperforms state-of-the-art methods by a large margin on several datasets and code sizes. For example, QINCo achieves better nearest-neighbor search accuracy using 12 bytes codes than other methods using 16 bytes on the BigANN and Deep1B dataset.

翻译：向量量化是数据压缩和向量搜索中的基本操作。为实现高精度，多码本方法通过跨多个码本使用码字表示每个向量来提升编码速率。残差量化（RQ）是此类方法之一，通过迭代量化前一步的残差误差来提升精度。误差分布依赖于先前选择的码字。然而，传统RQ未考虑这种依赖性，因其在每个量化步骤中使用通用码本。本文提出QINCo——一种神经RQ变体，通过使用条件于前序步骤向量近似值的神经网络为每个向量预测专用码本。实验表明，在多个数据集和码本规模上，QINCo显著优于现有最优方法。例如，在BigANN和Deep1B数据集中，QINCo使用12字节码即可实现比使用16字节码的其他方法更优的近邻搜索精度。