Conditioned Generation

Conditioned generation focuses on creating models that generate outputs based on specific input conditions, going beyond simply modeling data distributions. Current research explores diverse applications, from generating protein sequences with desired properties and synthesizing data for low-resource languages to creating stylized images and optimizing computer code. Key approaches involve leveraging large language models, diffusion models, and generative adversarial networks, often incorporating techniques like classifier guidance and self-supervised learning to improve generation quality and control. This field is significant for its potential to accelerate scientific discovery (e.g., protein design) and improve various technological applications (e.g., natural language processing, computer vision).