Deep neural networks (DNNs) are the workhorses of deep learning, which constitutes the
state of the art in numerous application domains. However, DNN-based decision rules are …

Despite the unchallenged performance, deep neural network (DNN) based object detectors
(OD) for computer vision have inherent, hard-to-verify limitations like brittleness, opacity, and …

The availability of representative datasets is an essential prerequisite for many successful
artificial intelligence and machine learning models. However, in real life applications these …

The behavior of neural networks (NNs) on previously unseen types of data (out-of-
distribution or OOD) is typically unpredictable. This can be dangerous if the network's output …

Deep neural networks (DNNs) play a crucial role in the field of machine learning,
demonstrating state-of-the-art performance across various application domains. However …

For machine learning components used as part of autonomous systems (AS) in carrying out
critical tasks it is crucial that assurance of the models can be maintained in the face of post …

In recent years, accurate and real-time long-range small target detection has become a
popular and challenging task, particularly in time-sensitive scenarios such as unmanned …

The evaluation and verification of neural networks (NNs), as a part of their safe design and
deployment, becomes a hot research topic, particularly with the recent studies showing their …