An improved transformer model based on global and local features for few-shot learning with noise samples

Yingjian Jiang

doi:10.1117/12.3004881

9 October 2023 An improved transformer model based on global and local features for few-shot learning with noise samples

Yingjian Jiang

Author Affiliations +

Proceedings Volume 12791, Third International Conference on Advanced Algorithms and Neural Networks (AANN 2023); 127911U (2023) https://doi.org/10.1117/12.3004881
Event: Third International Conference on Advanced Algorithms and Neural Networks (AANN 2023), 2023, Qingdao, SD, China

Abstract

It is a challenging problem to identify new categories with few support samples. In the current few-shot learning tasks, it is usually assumed that the support set is accurate, but in real scenarios, the support set may still have noisy samples. Therefore, how to learn a reliable model on the noisy support set is the direction of current research. To address the situation in few-shot learning task where the support set contains noisy samples, we propose a Transformer model for noisy few-shot learning that considers both global and local features. Specifically, we consider the difference between local features and global features between noise samples and support set samples, then align the local features of the samples, and use the attention mechanism of the transformer model to reduce the interference of noise samples to the model. To evaluate the effectiveness of our proposed models, we extensively test these methods on noisy versions of MiniImageNet and TieredImageNet. Our results show that the model performs competitively in the presence of label noise.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

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Yingjian Jiang "An improved transformer model based on global and local features for few-shot learning with noise samples", Proc. SPIE 12791, Third International Conference on Advanced Algorithms and Neural Networks (AANN 2023), 127911U (9 October 2023); https://doi.org/10.1117/12.3004881

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KEYWORDS

Statistical modeling

Transformers

Prototyping

Education and training

Feature extraction

Data modeling

Windows

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