Abstract Existing approaches to Domain Incremental Learning (DIL) address catastrophic
forgetting by storing and rehearsing exemplars from old domains. However, exemplar-based …

Existing fault diagnosis methods for rolling bearings are mostly applied in static domains
and cannot adapt to continuous and dynamic industrial data in practice. In domain …

The task of incremental learning is to enable machine learning models to continuously learn
and adapt to new tasks and data in changing environments while maintaining knowledge of …

Although neural networks have been used extensively in pattern recognition scenarios, the
pre-acquisition of datasets is still challenging. In most pattern recognition areas, preparing a …

Humans have the ability to constantly learn new knowledge. However, for artificial
intelligence, trying to continuously learn new knowledge usually results in catastrophic …

Radio frequency fingerprints are commonly exploited as a unique signature in the physical
layer for distinguishing transmitters in transmitter identification systems (TISs). In response to …

In complex mobility systems, the widespread presence of spatiotemporal anomaly patterns
poses substantial challenges to effective governance and decision-making. A notable …

Semi-supervised learning (SSL) has achieved significant success due to its capacity to
alleviate annotation dependencies. Most existing SSL methods utilize pseudo-labeling to …

Abstract Knowledge distillation is one of the most persuasive approaches to model
compression that transfers the representational expertise from large deep-learning teacher …

With the development of deep learning, the unsupervised visual anomaly detection and
localization task has gained significant attention in both academia and industry, where only …