Any given AI system cannot be accepted unless its trustworthiness is proven. An important
characteristic of a trustworthy AI system is the absence of algorithmic bias.'Individual …

Recent advancements in artificial intelligence (AI) technology have raised concerns about
the ethical, moral, and legal safeguards. There is a pressing need to improve metrics for …

Labeling objects at the subordinate level typically requires expert knowledge, which is not
always available from a random annotator. Accordingly, learning directly from web images …

Mass production of high-quality synthetic SAR training imagery is essential for boosting the
performance of deep-learning (DL)-based SAR automatic target recognition (ATR) …

Traditional pattern recognition models usually assume a fixed and identical number of
classes during both training and inference stages. In this paper, we study an interesting but …

Detecting out-of-distribution (OOD) data is a task that is receiving an increasing amount of
research attention in the domain of deep learning for computer vision. However, the …

How can we automatically select an out-of-distribution (OOD) detection model for various
underlying tasks? This is crucial for maintaining the reliability of open-world applications by …

Unsupervised outlier detection, which predicts if a test sample is an outlier or not using only
the information from unlabelled inlier data, is an important but challenging task. Recently …

Nowadays statistical machine learning is widely adopted in various domains such as data
mining, image recognition and automated driving. However, software quality assurance for …

The ability to detect Out-of-Distribution (OOD) data is important in safety-critical applications
of deep learning. The aim is to separate In-Distribution (ID) data drawn from the training …