Enhancing Generative Class Incremental Learning Performance With a Model Forgetting Approach

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 it is considered o...

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Bibliographic Details
Main Authors: Taro Togo, Ren Togo, Keisuke Maeda, Takahiro Ogawa, Miki Haseyama
Format: Article
Language:English
Published: IEEE 2025-01-01
Series:IEEE Open Journal of Signal Processing
Subjects:
Online Access:https://ieeexplore.ieee.org/document/10840249/
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