Xia Fang

Distributional Matching for Vector Quantization: A Unified Theoretical and Empirical Framework

This work introduces principled criteria for desirable VQ behavior and demonstrates that aligning feature and code vector distributions provides a unifying mechanism for mitigating training instability and codebook collapse, and instantiate this framework using a Wasserstein-based objective with an efficient closed-form under a mild Gaussian approximation.