Semantic-guided distribution calibrating for few-shot classification
The meta-learning paradigm is a mainstream method to solve the problem of few-shot learning. It constructs multiple tasks through episodic training so that the model can quickly generalize to new tasks. However, episodic training only sample a few training instances (shots) per class for simulating...
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| 格式: | Thesis-Master by Coursework |
| 語言: | English |
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Nanyang Technological University
2022
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| 在線閱讀: | https://hdl.handle.net/10356/155041 |
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| 機構: | Nanyang Technological University |
| 語言: | English |