Sequence Diffusion

Sequence diffusion models are emerging as powerful tools for generating and analyzing sequential data across diverse fields. Current research focuses on adapting diffusion model architectures, such as U-Nets and transformers, to handle discrete sequences and incorporate conditional information, often leveraging multimodal inputs or incorporating sequence-aware mechanisms. These advancements are improving the generation of realistic sequences in applications ranging from autonomous driving and sign language production to medical image analysis and protein structure prediction. The ability to effectively model and generate sequences holds significant promise for advancing various scientific domains and enabling new technological applications.