  Current state-of-the-art few-shot learners focus on developing effective
training procedures for feature representations, before using simple, e.g.
nearest centroid, classifiers. In this paper, we take an orthogonal approach
that is agnostic to the features used and focus exclusively on meta-learning
the actual classifier layer. Specifically, we introduce MetaQDA, a Bayesian
meta-learning generalization of the classic quadratic discriminant analysis.
This setup has several benefits of interest to practitioners: meta-learning is
fast and memory-efficient, without the need to fine-tune features. It is
agnostic to the off-the-shelf features chosen and thus will continue to benefit
from advances in feature representations. Empirically, it leads to robust
performance in cross-domain few-shot learning and, crucially for real-world
applications, it leads to better uncertainty calibration in predictions.
