The rise in deep neural networks (DNNs) has led to increased interest in explaining their
predictions. While many methods for this exist, there is currently no consensus on how to …

Learning representations that clearly distinguish between normal and abnormal data is key
to the success of anomaly detection. Most of existing anomaly detection algorithms use …

Traffic signs are critical for maintaining the safety and efficiency of our roads. Therefore, we
need to carefully assess the capabilities and limitations of automated traffic sign detection …

Visual explanations are logical arguments based on visual features that justify the
predictions made by neural networks. Current modes of visual explanations answer …

In active learning, acquisition functions define informativeness directly on the representation
position within the model manifold. However, for most machine learning models (in …

In this paper, we visualize and quantify the predictive uncertainty of gradient-based post hoc
visual explanations for neural networks. Predictive uncertainty refers to the variability in the …

In this paper, we propose a model-based characterization of neural networks to detect novel
input types and conditions. Novelty detection is crucial to identify abnormal inputs that can …

In this article, we present a leap-forward expansion to the study of explainability in neural
networks by considering explanations as answers to abstract reasoning-based questions …

In this paper, we propose a novel selection strategy for contrastive learning for medical
images. On natural images, contrastive learning uses augmentations to select positive and …

State-of-the-art algorithms successfully localize and recognize traffic signs over existing
datasets, which are limited in terms of challenging condition type and severity. Therefore, it …