Revisiting Multi-Codebook Quantization.

Area of Science:

• Computer Science
• Machine Learning
• Data Mining

Background:

• Multi-Codebook Quantization (MCQ) offers theoretical advantages for Approximate Nearest Neighbor (ANN) search by generalizing existing methods.
• The NP-hard nature of MCQ's encoding process has limited its practical application, often requiring complex constraints or time-consuming heuristic algorithms.

Purpose of the Study:

• To develop an efficient and effective deep learning-based solution for the encoding problem in Multi-Codebook Quantization.
• To overcome the computational challenges associated with traditional MCQ encoding methods.

Main Methods:

• A novel deep learning architecture is proposed, simplifying the complex MCQ encoding problem into a feed-forward process.
• The encoding network is designed for minimal complexity, enabling rapid vector-to-binary code conversion.

Main Results:

• The proposed deep learning method achieves state-of-the-art performance on three benchmark datasets for ANN search.
• Encoding speed is dramatically improved, showing 11x to 38x faster performance compared to heuristic algorithms.

Conclusions:

• The deep learning approach provides a practical and highly efficient solution for Multi-Codebook Quantization in large-scale retrieval systems.
• This work demonstrates the potential of deep learning to address computationally intensive problems in ANN search.