Traditional machine learning, mainly supervised learning, follows the assumptions of closed-
world learning, ie, for each testing class, a training class is available. However, such …

Machine learning models often encounter samples that are diverged from the training
distribution. Failure to recognize an out-of-distribution (OOD) sample, and consequently …

In an out-of-distribution (OOD) detection problem, samples of known classes (also called in-
distribution classes) are used to train a special classifier. In testing, the classifier can (1) …

Classical close-set semantic segmentation networks have limited ability to detect out-of-
distribution (OOD) objects, which is important for safety-critical applications such as …

Open set recognition (OSR) aims to correctly recognize the known classes and reject the
unknown classes for increasing the reliability of the recognition system. The distance-based …

Open-set object recognition aims to identify if an object is from a class that has been
encountered during training or not. To perform open-set object recognition accurately, a key …

The classifiers that return compact acceptance regions are crucial for the success in
anomaly detection and open set recognition settings since we have to determine and reject …

Open World Object Detection (OWOD), simulating the real dynamic world where knowledge
grows continuously, attempts to detect both known and unknown classes and incrementally …

Abstract Machine Learning models based on Deep Neural Networks behave unpredictably
when presented with inputs that do not stem from the training distribution and sometimes …

Although deep learning methods have achieved great success in synthetic aperture radar
automatic target recognition (SAR ATR), their accuracies decline sharply, as new classes …