Qinco2: Vector Compression And Search With Improved Implicit Neural Codebooks

Abstract

Vector quantization is a fundamental technique for compression and large-scale nearest neighbor search. For high-accuracy operating points, multi-codebook quantization associates data vectors with one element from each of multiple codebooks. An example is residual quantization (RQ), which iteratively quantizes the residual error of previous steps. Dependencies between the different parts of the code are, however, ignored in RQ, which leads to suboptimal rate-distortion performance. QINCo recently addressed this inefficiency by using a neural network to determine the quantization codebook in RQ based on the vector reconstruction from previous steps. In this paper we introduce QINCo2 which extends and improves QINCo with (i) improved vector encoding using codeword pre-selection and beam-search, (ii) a fast approximate decoder leveraging codeword pairs to establish accurate short-lists for search, and (iii) an optimized training procedure and network architecture. We conduct experiments o

Stats

• citations0
• S2 citations—
• github stars0
• HF likes0
• heat score0.00
• arxiv keyvallaeys2025qinco2

Related papers

• Nearest Neighbor Search With Compact Codes: A Decoder Perspective (2021)3.58
• Interleaved Composite Quantization For High-dimensional Similarity Search (2019)0.00
• SAQ: Pushing The Limits Of Vector Quantization Through Code Adjustment And Dimension Segmentation (2025)0.00
• Lossless Compression Of Vector Ids For Approximate Nearest Neighbor Search (2025)11.11
• Polysemous Codes (2016)11.49
• Quantization Meets Projection: A Happy Marriage For Approximate K-nearest Neighbor Search (2024)0.00
• Practical And Asymptotically Optimal Quantization Of High-dimensional Vectors In Euclidean Space For Approximate Nearest Neighbor Search (2024)8.82
• Pqtable: Non-exhaustive Fast Search For Product-quantized Codes Using Hash Tables (2017)7.16