  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 meta-learn the confidence for
each query sample, to assign optimal weights to unlabeled queries such that
they improve the model's transductive inference performance on unseen tasks. We
achieve this by meta-learning an input-adaptive distance metric over a task
distribution under various model and data perturbations, which will enforce
consistency on the model predictions under diverse uncertainties for unseen
tasks. Moreover, we additionally suggest a regularization which explicitly
enforces the consistency on the predictions across the different dimensions of
a high-dimensional embedding vector. We validate our few-shot learning model
with meta-learned confidence on four benchmark datasets, on which it largely
outperforms strong recent baselines and obtains new state-of-the-art results.
Further application on semi-supervised few-shot learning tasks also yields
significant performance improvements over the baselines. The source code of our
algorithm is available at https://github.com/seongmin-kye/MCT.
