Vector Quantisation

Vector quantization (VQ) is a technique for discretizing continuous data into a finite set of codebook vectors, aiming to efficiently represent data while minimizing information loss. Current research focuses on improving VQ's performance in various applications, including audio and image compression, generative modeling (e.g., using VQ-VAEs and VQGANs), and robust segmentation, often by adapting existing architectures or developing novel algorithms like those incorporating normal distributions or dynamic quantization. These advancements enhance the effectiveness of VQ in diverse fields, leading to improvements in data representation, model efficiency, and robustness to noise and domain shifts.