Welcome to Jens Henriksson's Doctoral Thesis defence: Outlier Detection as a Safety Measure for Safety Critical Deep Learning
The aim of this thesis has been to study how to connect parameters from DL with verification and testing for safety critical applications, and what extensions are necessary to verify deep neural networks. More specifically, this thesis has investigated the use of outlier detection as one testing method to detect when the model is operating on unfamiliar data.
Deep learning (DL) has proven to be a valuable component in object detection and semantic segmentation tasks, as the techniques have shown significant performance gains compared to hand-made image processing algorithms. DL refers to an optimization process where a model learns properties and parameters itself through in iterative process running on labeled data. The resulting model contains abstract features that are unintuitive to explain, thus challenging to ensure that the model will work as intended in safety critical applications (SCA).
