Enhancing Generative Class Incremental Learning Performance with Model
  Forgetting Approach
Taro Togo Ren Togo Keisuke Maeda Takahiro Ogawa Miki Haseyama
This study presents a novel approach to Generative Class Incremental Learning
(GCIL) by introducing the forgetting mechanism, aimed at dynamically managing
class information for better adaptation to streaming data. GCIL is one of the
hot topics in the field of computer vision, and this is considered one of the
crucial tasks in society, specifically the continual learning of generative
models. The ability to forget is a crucial brain function that facilitates
continual learning by selectively discarding less relevant information for
humans. However, in the field of machine learning models, the concept of
intentionally forgetting has not been extensively investigated. In this study
we aim to bridge this gap by incorporating the forgetting mechanisms into GCIL,
thereby examining their impact on the models' ability to learn in continual
learning. Through our experiments, we have found that integrating the
forgetting mechanisms significantly enhances the models' performance in
acquiring new knowledge, underscoring the positive role that strategic
forgetting plays in the process of continual learning.