Sampling-based Uncertainty Estimation for an Instance Segmentation
  Network
Florian Heidecker Ahmad El-Khateeb Bernhard Sick
The examination of uncertainty in the predictions of machine learning (ML)
models is receiving increasing attention. One uncertainty modeling technique
used for this purpose is Monte-Carlo (MC)-Dropout, where repeated predictions
are generated for a single input. Therefore, clustering is required to describe
the resulting uncertainty, but only through efficient clustering is it possible
to describe the uncertainty from the model attached to each object. This
article uses Bayesian Gaussian Mixture (BGM) to solve this problem. In
addition, we investigate different values for the dropout rate and other
techniques, such as focal loss and calibration, which we integrate into the
Mask-RCNN model to obtain the most accurate uncertainty approximation of each
instance and showcase it graphically.