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Erschienen in:
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2022 | OriginalPaper | Buchkapitel

Review of Few-Shot Learning in the Text Domain and the Image Domain

verfasst von : Zihang Zhang, Yuling Liu, Junwei Huang

Erschienen in: Advances in Artificial Intelligence and Security

Verlag: Springer International Publishing

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Abstract

Classical machine learning works ineffectively when the data set is small. Recently, few-shot learning is proposed to solve this problem. Few-shot learning models a few samples through the prior knowledge. We could divide few-shot learning into various categories depending on where the prior knowledge is extracted from. There are mainly three classes in this paper: (i) the prior knowledge extracted from the labeled data; (ii) the prior knowledge extracted from a weakly labeled or unlabeled data set; (iii) the prior knowledge extracted from similar data sets. For the convenience of searching corresponding few-shot learning methods in a certain domain, based on the above classification, we further classify few-shot learning models into ones which are applied to the image domain and the other which are applied to the text domain. With this taxonomy, we review the previous works on few-shot learning and discuss them according to these categories. Finally, present challenges and promising directions, in the aspect of few-shot learning, are also proposed.

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Vorheriges Kapitel A Text Multi-label Classification Scheme Based on Resampling and Ensemble Learning
Nächstes Kapitel A Fire Detection System Based on YOLOv4
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Metadaten
Titel
Review of Few-Shot Learning in the Text Domain and the Image Domain
verfasst von
Zihang Zhang
Yuling Liu
Junwei Huang
Copyright-Jahr
2022
Buch
Advances in Artificial Intelligence and Security

Print ISBN: 978-3-031-06760-0

Electronic ISBN: 978-3-031-06761-7

Copyright-Jahr: 2022

DOI
https://doi.org/10.1007/978-3-031-06761-7_7

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