Modern artificial intelligence systems require class-incremental learning while suffering from
catastrophic forgetting in many real-world applications. Due to the missing knowledge of …

Traffic Classification (TC) is experiencing a renewed interest, fostered by the growing
popularity of Deep Learning (DL) approaches. In exchange for their proved effectiveness …

Deep neural networks (DNNs) are prone to the notorious catastrophic forgetting problem
when learning new tasks incrementally. Class-incremental learning (CIL) is a promising …

As a front-burner problem in incremental learning, class incremental semantic segmentation
(CISS) is plagued by catastrophic forgetting and semantic drift. Although recent methods …

Abstract Person re-identification (ReID), considered as a sub-problem of image retrieval, is
critical for intelligent security. The general practice is to train a deep model on images from a …

The recent popularity growth of Deep Learning (DL) re-ignited the interest towards traffic
classification, with several studies demonstrating the accuracy of DL-based classifiers to …

Radio-Frequency (RF) based human activity recognition (HAR) enables many attractive
applications such as smart home, health monitoring, and virtual reality (VR). Among multiple …

Abstract Exemplar-free Class Incremental Learning (EFCIL) aims to sequentially learn tasks
with access only to data from the current one. EFCIL is of interest because it mitigates …

When optimizing sequentially incoming tasks, deep neural networks generally suffer from
catastrophic forgetting due to their lack of ability to maintain knowledge from old tasks. This …

Class-incremental learning (CIL) under an exemplar-free constraint has presented a
significant challenge. Existing methods adhering to this constraint are prone to catastrophic …