Supervised Continual learning involves updating a deep neural network (DNN) from an ever-
growing stream of labeled data. While most work has focused on overcoming catastrophic …

Online class-incremental learning (OCIL) studies the problem of mitigating the phenomenon
of catastrophic forgetting while learning new classes from a continuously non-stationary data …

Motivated by the efficiency and rapid convergence of pre-trained models for solving
downstream tasks, this paper extensively studies the impact of Continual Learning (CL) …

Deep neural networks suffer from catastrophic forgetting when trained on sequential tasks in
continual learning. Various methods rely on storing data of previous tasks to mitigate …

Sensor-based human activity recognition (HAR), ie, the ability to discover human daily
activity patterns from wearable or embedded sensors, is a key enabler for many real-world …

Deep learning based food recognition has achieved remarkable progress in predicting food
types given an eating occasion image. However, there are two major obstacles that hinder …

Session-based recommendation has received growing attention recently due to the
increasing privacy concern. Despite the recent success of neural session-based …

Graphs have been widely adopted to accomplish fraud detection tasks because of their
inherently favorable structure to capture the intricate features in many complicated …

Online continual learning (CL) aims to learn new knowledge and consolidate previously
learned knowledge from non-stationary data streams. Due to the time-varying training …

Class-Incremental Learning (CIL) or continual learning is a desired capability in the real
world, which requires a learning system to adapt to new tasks without forgetting former ones …