Anomaly detection is a critical issue across several academic fields and real-world
applications. Artificial neural networks have been proposed to detect anomalies from …

Generalized zero-shot learning (GZSL) aims at training a model that can generalize to
unseen class data by only using auxiliary information. One of the main challenges in GZSL …

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

In this paper, we advocate for two stages in a neural network's decision making process. The
first is the existing feed-forward inference framework where patterns in given data are …

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

Anomaly detection plays a key role in industrial manufacturing for product quality control.
Traditional methods for anomaly detection are rule-based with limited generalization ability …

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 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 …

Novelty detection aims to automatically identify out-of-distribution (OOD) data, without any
prior knowledge of them. It is a critical step in data monitoring, behavior analysis and other …

Novelty detection is a key capability for practical machine learning in the real world, where
models operate in non-stationary conditions and are repeatedly exposed to new, unseen …