Real-world applications require the classification model to adapt to new classes without
forgetting old ones. Correspondingly, Class-Incremental Learning (CIL) aims to train a …

Many real-world machine learning systems require the ability to continually learn new
knowledge. Class incremental learning receives increasing attention recently as a solution …

The problem of class incremental learning (CIL) is considered. State-of-the-art approaches
use a dynamic architecture based on network expansion (NE), in which a task expert is …

The ability to incrementally learn new classes is crucial to the development of real-world
artificial intelligence systems. In this paper, we focus on a challenging but practical few-shot …

In this paper, we address the incremental classifier learning problem, which suffers from
catastrophic forgetting. The main reason for catastrophic forgetting is that the past data are …

Class-incremental learning (CIL) has been widely studied under the setting of starting from a
small number of classes (base classes). Instead, we explore an understudied real-world …

In class-incremental learning, the model is expected to learn new classes continually while
maintaining knowledge on previous classes. The challenge here lies in preserving the …

Incremental learning attempts to develop a classifier which learns continuously from a
stream of data segregated into different classes. Deep learning approaches suffer from …

Deep learning models often suffer from forgetting previously learned information when
trained on new data. This problem is exacerbated in federated learning (FL), where the data …

Deep learning models suffer from catastrophic forgetting when trained in an incremental
learning setting. In this work, we propose a novel approach to address the task incremental …