Less confidence, less forgetting: Learning with a humbler teacher in exemplar-free Class-Incremental learning.
Neural Netw
; 179: 106513, 2024 Nov.
Article
em En
| MEDLINE
| ID: mdl-39018945
ABSTRACT
Class-Incremental learning (CIL) is challenging due to catastrophic forgetting (CF), which escalates in exemplar-free scenarios. To mitigate CF, Knowledge Distillation (KD), which leverages old models as teacher models, has been widely employed in CIL. However, based on a case study, our investigation reveals that the teacher model exhibits over-confidence in unseen new samples. In this article, we conduct empirical experiments and provide theoretical analysis to investigate the over-confident phenomenon and the impact of KD in exemplar-free CIL, where access to old samples is unavailable. Building on our analysis, we propose a novel approach, Learning with Humbler Teacher, by systematically selecting an appropriate checkpoint model as a humbler teacher to mitigate CF. Furthermore, we explore utilizing the nuclear norm to obtain an appropriate temporal ensemble to enhance model stability. Notably, LwHT outperforms the state-of-the-art approach by a significant margin of 10.41%, 6.56%, and 4.31% in various settings while demonstrating superior model plasticity.
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MEDLINE
Assunto principal:
Aprendizagem
Limite:
Humans
Idioma:
En
Ano de publicação:
2024
Tipo de documento:
Article