Rebalancing Multi-Label Class-Incremental Learning

• Published in: arXiv.org
• Main Authors: Du, Kaile, Zhou, Yifan, Lyu, Fan, Li, Yuyang, Xie, Junzhou, Shen, Yixi, Hu, Fuyuan, Liu, Guangcan
• Format: Paper
• Language: English
• Published: Ithaca Cornell University Library, arXiv.org 22.08.2024
• Subjects: Labels; Learning; Performance enhancement

Multi-label class-incremental learning (MLCIL) is essential for real-world multi-label applications, allowing models to learn new labels while retaining previously learned knowledge continuously. However, recent MLCIL approaches can only achieve suboptimal performance due to the oversight of the pos...