  In few-shot learning, a machine learning system learns from a small set of
labelled examples relating to a specific task, such that it can generalize to
new examples of the same task. Given the limited availability of labelled
examples in such tasks, we wish to make use of all the information we can.
Usually a model learns task-specific information from a small training-set
(support-set) to predict on an unlabelled validation set (target-set). The
target-set contains additional task-specific information which is not utilized
by existing few-shot learning methods. Making use of the target-set examples
via transductive learning requires approaches beyond the current methods; at
inference time, the target-set contains only unlabelled input data-points, and
so discriminative learning cannot be used. In this paper, we propose a
framework called Self-Critique and Adapt or SCA, which learns to learn a
label-free loss function, parameterized as a neural network. A base-model
learns on a support-set using existing methods (e.g. stochastic gradient
descent combined with the cross-entropy loss), and then is updated for the
incoming target-task using the learnt loss function. This label-free loss
function is itself optimized such that the learnt model achieves higher
generalization performance. Experiments demonstrate that SCA offers
substantially reduced error-rates compared to baselines which only adapt on the
support-set, and results in state of the art benchmark performance on
Mini-ImageNet and Caltech-UCSD Birds 200.
