TY  - JOUR
T1  - Efficient Prompt Learning Method in Blurry Class Incremental Learning Environment
AU  - Oh, Yunseok 
AU  - Choi, Dong-Wan 
JO  - Journal of KIISE, JOK
PY  - 2024
DA  - 2024/1/14
DO  - 10.5626/JOK.2024.51.7.655
KW  - continual learning
KW  - blurry-class incremental learning
KW  - prompt-based incremental learning
KW  - Vision Transformer
AB  - Continual learning is the process of continuously integrating new knowledge to maintain performance across a sequence of tasks. While disjoint continual learning, which assumes no overlap between classes across tasks, blurry continual learning addresses more realistic scenarios where overlaps do exist. Traditionally, most related works have predominantly focused on disjoint scenarios and recent attention has shifted towards prompt-based continual learning. This approach uses prompt mechanism within a Vision Transformer (ViT) model to improve adaptability. In this study, we analyze the effectiveness of a similarity function designed for blurry class incremental learning, applied within a prompt-based continual learning framework. Our experiments demonstrate the success of this method, particularly in its superior ability to learn from and interpret blurry data.