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Effective Decision Boundary Learning for Class Incremental Learning
April 24, 2024, 4:43 a.m. | Chaoyue Ding, Kunchi Li, Jun Wan, Shan Yu
cs.LG updates on arXiv.org arxiv.org
Abstract: Rehearsal approaches in class incremental learning (CIL) suffer from decision boundary overfitting to new classes, which is mainly caused by two factors: insufficiency of old classes data for knowledge distillation and imbalanced data learning between the learned and new classes because of the limited storage memory. In this work, we present a simple but effective approach to tackle these two factors. First, we employ a re-sampling strategy and Mixup K}nowledge D}istillation (Re-MKD) to improve the …
abstract arxiv class cs.cv cs.lg data decision distillation incremental knowledge memory overfitting storage type
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