Surrogate- and invariance-boosted contrastive learning for data-scarce applications in science
Deep learning techniques usually require a large quantity of training data and may be challenging for scarce datasets. The authors propose a framework that involves contrastive and transfer learning and reduces data requirements for training while keeping the prediction accuracy.
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2022-07-01
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| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-022-31915-y |