Vector Quantized

Vector quantization (VQ) is a technique used to represent continuous data, such as images or text, as discrete tokens, facilitating efficient processing by models like transformers. Current research focuses on improving VQ tokenizers by optimizing compression strategies, balancing semantic information preservation with detail retention, and exploring alternative training objectives to enhance downstream task performance in various applications, including image generation and language modeling. These advancements are driving improvements in model efficiency, speed, and overall quality, particularly for resource-constrained environments and high-resolution image generation.