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 …

Continual learning can empower vision-language models to continuously acquire new
knowledge without the need for access to the entire historical dataset. However mitigating …

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

This survey presents an in-depth exploration of knowledge distillation (KD) techniques
within the realm of Large Language Models (LLMs), spotlighting the pivotal role of KD in …

Deep Learning models have achieved remarkable performance in tasks such as image
classification or generation, often surpassing human accuracy. However, they can struggle …

We present a novel semantic segmentation approach for incremental nuclei segmentation
from histopathological images which is a very challenging task as we have to incrementally …

Although neural networks have been used extensively in pattern recognition scenarios, the
pre-acquisition of datasets is still challenging. In most pattern recognition areas, preparing a …

Abstract Non-Exemplar Class Incremental Learning (NECIL) involves learning a
classification model on a sequence of data without access to exemplars from previously …

Existing vision-based photovoltaic cell defect detection methods usually update models with
all defect data of both old and new categories to adapt to new classes emerging in the …

Continual learning strives to acquire knowledge across sequential tasks without forgetting
previously assimilated knowledge. Current state-of-the-art methodologies utilize dynamic …