Partitioned Memory Storage Inspired Few-Shot Class-Incremental learning

• URL: http://arxiv.org/abs/2504.20797v1
• Date: Tue, 29 Apr 2025 14:11:06 GMT
• Title: Partitioned Memory Storage Inspired Few-Shot Class-Incremental learning
• Authors: Renye Zhang, Yimin Yin, Jinghua Zhang,
• Abstract summary: Few-Shot Class-Incremental Learning (FSCIL) focuses on continuous learning of new categories with limited samples without forgetting old knowledge.<n>Our paper develops a method that learns independent models for each session. It can inherently prevent catastrophic forgetting.<n>Our method provides a fresh viewpoint for FSCIL and demonstrates the state-of-the-art performance on CIFAR-100 and mini-ImageNet datasets.
• Score: 2.9845592719739127
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Current mainstream deep learning techniques exhibit an over-reliance on extensive training data and a lack of adaptability to the dynamic world, marking a considerable disparity from human intelligence. To bridge this gap, Few-Shot Class-Incremental Learning (FSCIL) has emerged, focusing on continuous learning of new categories with limited samples without forgetting old knowledge. Existing FSCIL studies typically use a single model to learn knowledge across all sessions, inevitably leading to the stability-plasticity dilemma. Unlike machines, humans store varied knowledge in different cerebral cortices. Inspired by this characteristic, our paper aims to develop a method that learns independent models for each session. It can inherently prevent catastrophic forgetting. During the testing stage, our method integrates Uncertainty Quantification (UQ) for model deployment. Our method provides a fresh viewpoint for FSCIL and demonstrates the state-of-the-art performance on CIFAR-100 and mini-ImageNet datasets.

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