To cope with real-world dynamics, an intelligent system needs to incrementally acquire,
update, accumulate, and exploit knowledge throughout its lifetime. This ability, known as …

In contrast to batch learning where all training data is available at once, continual learning
represents a family of methods that accumulate knowledge and learn continuously with data …

Unlike previous studies on the Metaverse based on Second Life, the current Metaverse is
based on the social value of Generation Z that online and offline selves are not different …

Deep models, eg, CNNs and Vision Transformers, have achieved impressive achievements
in many vision tasks in the closed world. However, novel classes emerge from time to time in …

The lifelong learning paradigm in machine learning is an attractive alternative to the more
prominent isolated learning scheme not only due to its resemblance to biological learning …

In class incremental learning (CIL) a model must learn new classes in a sequential manner
without forgetting old ones. However, conventional CIL methods consider a balanced …

Transfer learning from large-scale pre-trained models has become essential for many
computer vision tasks. Recent studies have shown that datasets like ImageNet are weakly …

Current deep reinforcement learning (RL) algorithms are still highly task-specific and lack
the ability to generalize to new environments. Lifelong learning (LLL), however, aims at …

The subpopulation shifting challenge, known as some subpopulations of a category that are
not seen during training, severely limits the classification performance of the state-of-the-art …

Different from fine-tuning models pre-trained on a large-scale dataset of preset classes,
class-incremental learning (CIL) aims to recognize novel classes over time without forgetting …