3 citations · 5 across the 7 of their papers we have counts for
4 papers · 1 filter
Noise-Robust Conditional Flow Matching: Generating Clean Samples from Noisy Datasets
Adrian Urbański, Gabriel della Maggiora, Artur Yakimovich
Generative models learn the statistical properties of their training data, so high-quality generation depends on clean and representative datasets. In scientific imaging, acquisiti…
Metric-Guided Synthetic Image Data Rendering for Deep Learning compatible with Agentic AI
Martina Radoynova, Samuel Pantze, Trina De +2
Deep learning computer vision for scientific applications requires collecting and annotating large datasets in a laborious, expensive and error-prone process. Synthetic data genera…
Single-shot Star-convex Polygon-based Instance Segmentation for Spatially-correlated Biomedical Objects
Trina De, Adrian Urbanski, Artur Yakimovich
Biomedical images often contain objects known to be spatially correlated or nested due to their inherent properties, leading to semantic relations. Examples include cell nuclei bei…
Conditional Variational Diffusion Models
Gabriel della Maggiora, Luis Alberto Croquevielle, Nikita Deshpande +3
Inverse problems aim to determine parameters from observations, a crucial task in engineering and science. Lately, generative models, especially diffusion models, have gained popul…