Type: Master Thesis
Student: Jyotirmay Senapati
Supervisor: Abhijit Guha Roy, Sebastian Pölsterl, Nassir Navab, Christian Wachinger
Status: Finished on 02.04.2020
Abstract:
Bayesian Deep Learning has gained a lot of popularity in the Deep Learning community due to its ability to generate well calibrated outputs by associating a confidence score along with the prediction. This has high implications in using deep learning for safety critical applications like Medical diagnosis, Autonomous driving etc. Recently, alot of work has explored and proposed different strategies for variational inference of Deep Networks to estimate the confidence. It is very difficult to say which is better for what application. Towards this end we explore these different strategies to identify their effectiveness targeting the application of segmentation Quality Control.