Future communication networks are envisioned to satisfy increasingly granular and dynamic
requirements to accommodate the application and user demands. Indeed, novel immersive …

Image deraining is a challenging task since rain streaks have the characteristics of a
spatially long structure and have a complex diversity. Existing deep learning-based methods …

Artificial neural networks are prone to suffer from catastrophic forgetting. Networks trained on
something new tend to rapidly forget what was learned previously, a common phenomenon …

This paper proposes an assessor-guided learning strategy for continual learning where an
assessor guides the learning process of a base learner by controlling the direction and pace …

Continual learning can incrementally absorb new concepts without interfering with
previously learned knowledge. Motivated by the characteristics of neural networks, in which …

Like generic multi-task learning, continual learning has the nature of multi-objective
optimization, and therefore faces a trade-off between the performance of different tasks. That …

Artificial neural networks, celebrated for their human-like cognitive learning abilities, often
encounter the well-known catastrophic forgetting (CF) problem, where the neural networks …

Abstract Few-Shot Class-Incremental Learning (FSCIL) presents an extension of the Class
Incremental Learning (CIL) problem where a model is faced with the problem of data scarcity …

Saliency prediction (SAP) plays a crucial role in simulating the visual perception function of
human beings. In practical situations, humans can quickly grasp saliency extraction in new …

The goal of continual learning is to provide intelligent agents that are capable of learning
continually a sequence of tasks using the knowledge obtained from previous tasks while …