In real-world recognition/classification tasks, limited by various objective factors, it is usually
difficult to collect training samples to exhaust all classes when training a recognizer or …

With the continuous increase in cyberattacks over the past few decades, the quest to
develop a comprehensive, robust, and effective intrusion detection system (IDS) in the …

Open-set classification is a problem of handling'unknown'classes that are not contained in
the training dataset, whereas traditional classifiers assume that only known classes appear …

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) …

We introduce the integrative task of few-shot classification and segmentation (FS-CS) that
aims to both classify and segment target objects in a query image when the target classes …

Traditional supervised learning makes the closed-world assumption that the classes
appeared in the test data must have appeared in training. This also applies to text learning …

Abstract The Out-of-Domain (OOD) intent classification is a basic and challenging task for
dialogue systems. Previous methods commonly restrict the region (in feature space) of In …

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 …

Open intent classification is a challenging task in dialogue systems. On the one hand, it
should ensure the quality of known intent identification. On the other hand, it needs to detect …

Identifying the unknown (novel) user intents that have never appeared in the training set is a
challenging task in the dialogue system. In this paper, we present a two-stage method for …