4 papers · 1 filter
Flow-Based Generative Modeling for Optimizing Sampling Policies in Compressed Sensing Applications
Roman Pavelkin, Luis A. Zavala-Mondragon, Christiaan G. A. Viviers +1
Numerous modern applications in signal processing and medical imaging necessitate acquiring high-dimensional signals under tight resource constraints. Traditional sampling theory s…
A Review of Bayesian Uncertainty Quantification in Deep Probabilistic Image Segmentation
M. M. A. Valiuddin, R. J. G. van Sloun, C. G. A. Viviers +2
Advances in architectural design, data availability, and compute have driven remarkable progress in semantic segmentation. Yet, these models often rely on relaxed Bayesian assumpti…
Improving Aleatoric Uncertainty Quantification in Multi-Annotated Medical Image Segmentation with Normalizing Flows
M. M. A. Valiuddin, C. G. A. Viviers, R. J. G. van Sloun +2
Quantifying uncertainty in medical image segmentation applications is essential, as it is often connected to vital decision-making. Compelling attempts have been made in quantifyin…
Out-of-Distribution Detection of Melanoma using Normalizing Flows
M. M. A. Valiuddin, C. G. A. Viviers
Generative modelling has been a topic at the forefront of machine learning research for a substantial amount of time. With the recent success in the field of machine learning, espe…