Abstract The field of Continual Learning investigates the ability to learn consecutive tasks
without losing performance on those previously learned. The efforts of researchers have …

Artificial neural networks (ANNs) have emerged as an essential tool in machine learning,
achieving remarkable success across diverse domains, including image and speech …

Incrementally learning new information from a non-stationary stream of data, referred to as
'continual learning', is a key feature of natural intelligence, but a challenging problem for …

This work investigates the entanglement between Continual Learning (CL) and Transfer
Learning (TL). In particular, we shed light on the widespread application of network …

The goal of continual learning is to find a model that solves multiple learning tasks which are
presented sequentially to the learner. A key challenge in this setting is that the learner may" …

Computation offloading is considered as a promising method to improve computation
performance of Intelligent Vehicles (IVs), where IVs can offload resource-hungry …

The multi-layer structures of Deep Learning facilitate the processing of higher-level
abstractions from data, thus leading to improved generalization and widespread …

Continual learning (CL) trains NN models incrementally from a continuous stream of tasks.
To remember previously learned knowledge, prior studies store old samples over a memory …

Recent years have seen significant progress in unsupervised continual learning methods.
Despite their success in controlled settings, their practicality in real-world contexts remains …

Machine learning aims to generate a predictive model from a training dataset of a fixed
number of known classes. However, many real-world applications (such as health …