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arXiv · 2106.06410

What Can Knowledge Bring to Machine Learning? -- A Survey of Low-shot Learning for Structured Data

Yang Hu·Adriane Chapman·Guihua Wen·Dame Wendy Hall
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Abstract

Supervised machine learning has several drawbacks that make it difficult to use in many situations. Drawbacks include: heavy reliance on massive training data, limited generalizability and poor expressiveness of high-level semantics. Low-shot Learning attempts to address these drawbacks. Low-shot learning allows the model to obtain good predictive power with very little or no training data, where structured knowledge plays a key role as a high-level semantic representation of human. This article will review the fundamental factors of low-shot learning technologies, with a focus on the operation of structured knowledge under different low-shot conditions. We also introduce other techniques relevant to low-shot learning. Finally, we point out the limitations of low-shot learning, the prospects and gaps of industrial applications, and future research directions.

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Yang Hu, Adriane Chapman, Guihua Wen, Dame Wendy Hall. 2021-06-11. What Can Knowledge Bring to Machine Learning? -- A Survey of Low-shot Learning for Structured Data. https://arxiv.org/abs/2106.06410

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