@article{M527C986E,
title = "Efficient Prompt Learning Method in Blurry Class Incremental Learning Environment",
journal = "Journal of KIISE, JOK",
year = "2024",
issn = "2383-630X",
doi = "10.5626/JOK.2024.51.7.655",
author = "Yunseok Oh, Dong-Wan Choi",
keywords = "continual learning, blurry-class incremental learning, prompt-based incremental learning, Vision Transformer",
abstract = "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."
}