Transformer Based Continual Learning

Transformer-based continual learning focuses on enabling large transformer models to learn new tasks sequentially without forgetting previously acquired knowledge, a crucial challenge for deploying these models in real-world applications. Current research emphasizes efficient model architectures, such as dynamically expanding transformers and those employing knowledge distillation or prototype-based methods, to mitigate catastrophic forgetting and reduce computational overhead. These advancements are significant because they address the limitations of traditional training paradigms, paving the way for more adaptable and resource-efficient AI systems across various domains, including multimodal learning and object detection.