Few-Shot Learning
Last updated on November 12, 2024
Few-Shot learning is a machine learning paradigm that aims to increase the accuracy of a model by training on a small number of examples. This is not to be confused with Few-Shot Prompting, which more specifically refers to the prompting technique.
Footnotes
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Fei-Fei, L., Fergus, R., & Perona, P. (2006). One-shot learning of object categories. IEEE Transactions on Pattern Analysis and Machine Intelligence, 28(4), 594–611. ↩
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Wang, Y., Yao, Q., Kwok, J. T., & Ni, L. M. (2020). Generalizing from a few examples: A survey on few-shot learning. ACM Computing Surveys (Csur), 53(3), 1–34. ↩