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 …

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 …

As science attempts to close the gap between man and machine by building systems
capable of learning, we must embrace the importance of the unknown. The ability to …

Open Set Recognition (OSR) is about dealing with unknown situations that were not learned
by the models during training. In this paper, we provide a survey of existing works about …

Abstract Multi-Label Text Classification (MLTC) is an important but challenging task in the
field of natural language processing. In this paper, we propose a novel method, Semantic …

In recent years, remote sensing recognition technology has found extensive applications in
diverse fields, such as modern agriculture, forest management, urban planning, natural …

With the maturity and popularity of dialogue systems, detecting user's unknown intent in
dialogue systems has become an important task. It is also one of the most challenging tasks …

Specific emitter identification (SEI) can verify the identity of emitters and plays an important
role in a wide range of military and civilian fields. Most recently, there has been great …

Encrypted network traffic has been known to leak information about their underlying content
through side-channel information leaks. Traffic fingerprinting attacks exploit this by using …

Featured Application Signal Processing. Abstract Open-set signal recognition provides a
new approach for verifying the robustness of models by introducing novel unknown signal …