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Semi-supervised Few-shot Learning/Ushahidi
Rekodi ya ushahidi wa mbinu

Semi-supervised Few-shot Learning

Semi-supervised Few-shot Learning (SS-FSL) trains models to classify new classes from only a handful of labeled examples per class, while simultaneously leveraging a pool of unlabeled data to enrich class representations. By combining meta-learning episodes with soft pseudo-label assignment for unlabeled samples, it achieves notably higher accuracy than purely supervised few-shot methods when abundant unlabeled data is available.

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Semi-supervised Few-shot Learning (SS-FSL)
Rekodi ya mbinu ya kiajenda · ml-model / machine-learning
  • Ren, M., Triantafillou, E., Ravi, S., Snell, J., Swersky, K., Tenenbaum, J. B., Larochelle, H., & Zemel, R. S. (2018). Meta-learning for semi-supervised few-shot classification. In International Conference on Learning Representations (ICLR 2018). · URL
  • Finn, C., Abbeel, P., & Levine, S. (2017). Model-agnostic meta-learning for fast adaptation of deep networks. In Proceedings of the 34th International Conference on Machine Learning (ICML 2017), PMLR 70, 1126–1135. · URL
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Taxonomic bucketFew-shot Learningmachine-suggested · Relational suggestion, not evidence.Taxonomic bucketSelf-supervised Learningmachine-suggested · Relational suggestion, not evidence.Taxonomic bucketSemi-supervised Learningmachine-suggested · Relational suggestion, not evidence.Taxonomic bucketTransfer Learningmachine-suggested · Relational suggestion, not evidence.

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