Real-world scenarios are usually accompanied by continuously appearing classes with
scare labeled samples, which require the machine learning model to incrementally learn …

Abstract Class-Incremental Learning (CIL) requires a learning system to continually learn
new classes without forgetting. Despite the strong performance of Pre-Trained Models …

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 …

Exemplar-free class-incremental learning (CIL) poses several challenges since it prohibits
the rehearsal of data from previous tasks and thus suffers from catastrophic forgetting …

The incremental learning paradigm in machine learning has consistently been a focus of
academic research. It is similar to the way in which biological systems learn, and reduces …

Incremental learning aims to overcome catastrophic forgetting when learning deep networks
from sequential tasks. With impressive learning efficiency and performance, prompt-based …

Continual learning aims to learn a model from a continuous stream of data, but it mainly
assumes a fixed number of data and tasks with clear task boundaries. However, in real …

Class-incremental learning (CIL) aims to train a classification model while the number of
classes increases phase-by-phase. An inherent challenge of CIL is the stability-plasticity …

Neural networks suffer from catastrophic forgetting when sequentially learning tasks phase-
by-phase, making them inapplicable in dynamically updated systems. Class-incremental …

Non-exemplar class-incremental learning (NECIL) requires deep models to maintain
existing knowledge while continuously learning new classes without saving old class …