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• W3132732298 abstract "Few-shot classification has received great attention in the field of machine learning and computer vision. It aims is to achieve the learning ability close to human recognition by training from a few labelled samples. The existing few-shot classification methods have attempted to alleviate the impact of insufficient samples in a variety of ways, such as meta-learning and metric learning, but they ignore the noise robustness. This work proposes a new Anti-Noise Relation Network by embedding an autoencoder network into a classical neural network of fewshot classification, Relation Network. Experimental results on the Stanford Car and CUB-200-2011 datasets demonstrate the superiority of the proposed method in both classification accuracy and robustness against different noises." @default.
• W3132732298 created "2021-03-01" @default.
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• W3132732298 date "2020-12-07" @default.
• W3132732298 modified "2023-09-27" @default.
• W3132732298 title "Anti-Noise Relation Network for Few-shot Learning" @default.
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• W3132732298 hasPublicationYear "2020" @default.
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