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2022 | OriginalPaper | Buchkapitel

Improving Few-Shot Image Classification with Self-supervised Learning

verfasst von : Shisheng Deng, Dongping Liao, Xitong Gao, Juanjuan Zhao, Kejiang Ye

Erschienen in: Cloud Computing – CLOUD 2022

Verlag: Springer Nature Switzerland

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Abstract

Few-Shot Image Classification (FSIC) aims to learn an image classifier with only a few training samples. The key challenge of few-shot image classification is to learn this classifier with scarce labeled data. To tackle the issue, we leverage the self-supervised learning (SSL) paradigm to exploit unsupervised information. This work builds upon two-stage training paradigm, to push the current state-of-the-art (SOTA) in solving FSIC problem further. Specifically, we incorporate the traditional self-supervised learning method (TSSL) into the pre-training stage and propose an episodic contrastive loss (CL) as an auxiliary supervision for the meta-training stage. The proposed bipartite method, called FSIC-SSL, can SOTA task accuracies on two mainstream FSIC benchmark datasets. Our code will be available at https://github.com/SethDeng/FSIC_SSL.

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Titel: Improving Few-Shot Image Classification with Self-supervised Learning
verfasst von: Shisheng Deng
Dongping Liao
Xitong Gao
Juanjuan Zhao
Kejiang Ye
Verlag: Springer Nature Switzerland
Buch: Cloud Computing – CLOUD 2022
Print ISBN: 978-3-031-23497-2

Electronic ISBN: 978-3-031-23498-9

Copyright-Jahr: 2022
DOI: https://doi.org/10.1007/978-3-031-23498-9_5

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