3 citations · 5 across the 7 of their papers we have counts for
3 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…
Cryo-SWAN: the Multi-Scale Wavelet-decomposition-inspired Autoencoder Network for molecular density representation of molecular volumes
Rui Li, Artsemi Yushkevich, Mikhail Kudryashev +1
Learning robust representations of 3D shapes from voxelized data is essential for advancing AI methods in biomedical imaging. However, most contemporary 3D computer vision approach…