Abstract
Data-intensive applications have achieved great success in the field of machine learning. How to ensure that the machine can still learn correctly in the absence of labeled samples is the next challenging problem to be solved. This paper first introduces the problem definition of few-shot learning. Secondly, the existing small few-shot learning methods based on meta-learning are comprehensively summarized. Specifically, they are divided into three categories: metric-based learning methods, optimization-based learning methods and model-based learning methods. We conducted a series of comparisons among various methods in each category to show the advantages and disadvantages of each method. Finally, the limitations of existing methods are analyzed, and the future development direction of few-shot learning research is prospected.
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Huang, W., He, M., Wang, Y. (2022). A Survey on Meta-learning Based Few-Shot Classification. In: Jiang, X. (eds) Machine Learning and Intelligent Communications. MLICOM 2021. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 438. Springer, Cham. https://doi.org/10.1007/978-3-031-04409-0_23
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