Large deep learning models are impressive, but they struggle when real-time data is not
available. Few-shot class-incremental learning (FSCIL) poses a significant challenge for …

Forgetting refers to the loss or deterioration of previously acquired information or knowledge.
While the existing surveys on forgetting have primarily focused on continual learning …

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 …

Continual learning (CL) aims to incrementally learn different tasks (such as classification) in
a non-stationary data stream without forgetting old ones. Most CL works focus on tackling …

Abstract Vision-Language Pretraining (VLP) has shown impressive results on diverse
downstream tasks by offline training on large-scale datasets. Regarding the growing nature …

In class incremental learning (CIL) scenarios the phenomenon of catastrophic forgetting
caused by the classifier's bias towards the current task has long posed a significant …

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

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 …

Continual graph learning (CGL) is purposed to continuously update a graph model with
graph data being fed in a streaming manner. Since the model easily forgets previously …