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Learning with little data is challenging but often inevitable in various application scenarios where the labeled data is limited and costly.
Improving generalization with active learning
David Cohn, Les Atlas, and Richard Ladner · 1994
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Deep residual learning for image recognition
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Grad-cam: Visual explanations from deep networks via gradient-based localization
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Deep learning for anomaly detection: A survey
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Generalizing from a few examples: A survey on few-shot learning
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Aggregating nested transformers
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When vision transformers outperform resnets without pretraining or strong data augmentations
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Escaping the big data paradigm with compact transformers
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Budget-aware few-shot learning via graph convolutional network
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