  Few-shot learning has become essential for producing models that generalize
from few examples. In this work, we identify that metric scaling and metric
task conditioning are important to improve the performance of few-shot
algorithms. Our analysis reveals that simple metric scaling completely changes
the nature of few-shot algorithm parameter updates. Metric scaling provides
improvements up to 14% in accuracy for certain metrics on the mini-Imagenet
5-way 5-shot classification task. We further propose a simple and effective way
of conditioning a learner on the task sample set, resulting in learning a
task-dependent metric space. Moreover, we propose and empirically test a
practical end-to-end optimization procedure based on auxiliary task co-training
to learn a task-dependent metric space. The resulting few-shot learning model
based on the task-dependent scaled metric achieves state of the art on
mini-Imagenet. We confirm these results on another few-shot dataset that we
introduce in this paper based on CIFAR100. Our code is publicly available at
https://github.com/ElementAI/TADAM.
