Parham Saremi

Maîtrise recherche - McGill

Superviseur⋅e principal⋅e

Tal Arbel

Sujets de recherche

Apprentissage automatique médical

Apprentissage de représentations

Apprentissage profond

Modèles de diffusion

Modèles génératifs

Modèles probabilistes

Vision par ordinateur

Site web

Google Scholar

GitHub

Publications

Discovering Latent Graphs with GFlowNets for Diverse Conditional Image Generation

Bailey Trang

Parham Saremi

Alan Q. Wang

Fangrui Huang

Zahra TehraniNasab

Amar Kumar

Tal Arbel

Li Fei-Fei

Ehsan Adeli

Capturing diversity is crucial in conditional and prompt-based image generation, particularly when conditions contain uncertainty that can l… (voir plus)ead to multiple plausible outputs. To generate diverse images reflecting this diversity, traditional methods often modify random seeds, making it difficult to discern meaningful differences between samples, or diversify the input prompt, which is limited in verbally interpretable diversity. We propose Rainbow, a novel conditional image generation framework, applicable to any pretrained conditional generative model, that addresses inherent condition/prompt uncertainty and generates diverse plausible images. Rainbow is based on a simple yet effective idea: decomposing the input condition into diverse latent representations, each capturing an aspect of the uncertainty and generating a distinct image. First, we integrate a latent graph, parameterized by Generative Flow Networks (GFlowNets), into the prompt representation computation. Second, leveraging GFlowNets' advanced graph sampling capabilities to capture uncertainty and output diverse trajectories over the graph, we produce multiple trajectories that collectively represent the input condition, leading to diverse condition representations and corresponding output images. Evaluations on natural image and medical image datasets demonstrate Rainbow's improvement in both diversity and fidelity across image synthesis, image generation, and counterfactual generation tasks.

2025-09-17

NeurIPS.cc/2025/Conference (poster)

doi.org

openreview.net

Conditional Diffusion Models are Medical Image Classifiers that Provide Explainability and Uncertainty for Free

Gian Mario Favero

2025-03-26

MIDL.io/2025/Conference (présentation orale)