Yanzhao Zhou, Binghao Liu, Yiran Liu, Jianbin Jiao*
CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.1, pp. 647-668, 2023, DOI:10.32604/cmes.2023.026745
Abstract Deep Convolution Neural Networks (DCNNs) can capture discriminative features from large datasets. However, how to incrementally learn new samples without forgetting old ones and recognize novel classes that arise in the dynamically changing world, e.g., classifying newly discovered fish species, remains an open problem. We address an even more challenging and realistic setting of this problem where new class samples are insufficient, i.e., Few-Shot Class-Incremental Learning (FSCIL). Current FSCIL methods augment the training data to alleviate the overfitting of novel classes. By contrast, we propose Filter Bank Networks (FBNs) that augment the learnable filters to capture fine-detailed features for adapting… More >
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