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Trainable Class Prototypes for Few-Shot Learning
Metric learning is a widely used method for few shot learning in which the quality of prototypes plays a key role in the algorithm.
Few-Shot Image Classification via Contrastive Self-Supervised Learning
Most previous few-shot learning algorithms are based on meta-training with fake few-shot tasks as training samples, where large labeled base classes are required.
Ranked #10 on
Unsupervised Few-Shot Image Classification
on Mini-Imagenet 5-way (1-shot)
(using extra training data)
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