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Meta-Learned Confidence for Few-shot Learning
[article]
2020
arXiv
pre-print
Transductive inference is an effective means of tackling the data deficiency problem in few-shot learning settings. A popular transductive inference technique for few-shot metric-based approaches, is to update the prototype of each class with the mean of the most confident query examples, or confidence-weighted average of all the query samples. However, a caveat here is that the model confidence may be unreliable, which may lead to incorrect predictions. To tackle this issue, we propose to
arXiv:2002.12017v2
fatcat:iizg5agw2zexhaxfs3vip4ackq