Vector Quantization

Vector quantization (VQ) is a technique for representing continuous data using a discrete set of codebook vectors, aiming to achieve efficient data compression and representation learning. Current research focuses on improving VQ's performance in various applications, including image and audio generation, speech synthesis, and large language model compression, often employing novel architectures like autoregressive transformers and energy-based models to enhance accuracy and efficiency. The impact of VQ extends across diverse fields, from improving the interpretability and robustness of AI systems to enabling efficient compression and processing of large datasets in resource-constrained environments.