3 papers
cs.CV2025
Discovering Latent Graphs with GFlowNets for Diverse Conditional Image Generation
Bailey Trang, Parham Saremi, Alan Q. Wang +6
Capturing diversity is crucial in conditional and prompt-based image generation, particularly when conditions contain uncertainty that can lead to multiple plausible outputs. To ge…
cs.CV2025
RL4Med-DDPO: Reinforcement Learning for Controlled Guidance Towards Diverse Medical Image Generation using Vision-Language Foundation Models
Parham Saremi, Amar Kumar, Mohamed Mohamed +2
Vision-Language Foundation Models (VLFM) have shown a tremendous increase in performance in terms of generating high-resolution, photorealistic natural images. While VLFMs show a r…
cs.CV2025
Conditional Diffusion Models are Medical Image Classifiers that Provide Explainability and Uncertainty for Free
Gian Mario Favero, Parham Saremi, Emily Kaczmarek +2
Discriminative classifiers have become a foundational tool in deep learning for medical imaging, excelling at learning separable features of complex data distributions. However, th…