Discrete Token

Discrete tokens represent a crucial advancement in various machine learning domains, aiming to improve efficiency and performance by converting continuous data (like images, speech, or trajectories) into discrete symbolic representations. Current research focuses on applying discrete tokenization within self-supervised learning frameworks, particularly using autoregressive models and contrastive learning, to enhance tasks such as speech recognition, image generation, and multimodal understanding. This approach offers benefits including faster processing, reduced data storage needs, and improved model generalization, impacting fields ranging from natural language processing and computer vision to robotics and autonomous driving.