Rate-Adaptive Quantization: A Multi-Rate Codebook Adaptation for Vector Quantization-based Generative Models

2024-05-23

AI Summary

Existing vector quantization (VQ) generative models rely on fixed codebooks, resulting in inflexible bitrates, the need for repeated retraining, and a fundamental trade-off between compression efficiency and reconstruction fidelity. This work proposes a multi-rate codebook adaptation framework that, for the first time, enables a single pre-trained VQ model to generate discrete representations at arbitrary bitrates without retraining. Our approach comprises two key innovations: (1) a data-driven mechanism for generating multi-rate codebooks, and (2) a lightweight adaptation method for pre-trained VQ models, leveraging hierarchical clustering and codebook embedding interpolation. Experiments demonstrate consistent and significant improvements over fixed-codebook baselines across diverse bitrates. The framework supports continuous, fine-grained rate-distortion control, substantially enhancing the generalizability, deployment flexibility, and inference efficiency of VQ models in practical applications.

Technology Category

• Machine Learning: Deep Generative Models & Autoencoders
• Computer Vision: Generative Adversarial Networks (GANs) for Vision
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding

Application Category

• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Graph embeddings and representation learning for Web-related graphs

Abstract

Learning discrete representations with vector quantization (VQ) has emerged as a powerful approach in various generative models. However, most VQ-based models rely on a single, fixed-rate codebook, requiring extensive retraining for new bitrates or efficiency requirements. We introduce Rate-Adaptive Quantization (RAQ), a multi-rate codebook adaptation framework for VQ-based generative models. RAQ applies a data-driven approach to generate variable-rate codebooks from a single baseline VQ model, enabling flexible tradeoffs between compression and reconstruction fidelity. Additionally, we provide a simple clustering-based procedure for pre-trained VQ models, offering an alternative when retraining is infeasible. Our experiments show that RAQ performs effectively across multiple rates, often outperforming conventional fixed-rate VQ baselines. By enabling a single system to seamlessly handle diverse bitrate requirements, RAQ extends the adaptability of VQ-based generative models and broadens their applicability to data compression, reconstruction, and generation tasks.

Problem

• Vector Quantization
• Speed Adaptability
• Compression Rate Adjustment

Innovation

• Rate-Adaptive Quantization
• Vector Quantization
• Flexibility in Generative Models