  Few-shot recognition involves training an image classifier to distinguish
novel concepts at test time using few examples (shot). Existing approaches
generally assume that the shot number at test time is known in advance. This is
not realistic, and the performance of a popular and foundational method has
been shown to suffer when train and test shots do not match. We conduct a
systematic empirical study of this phenomenon. In line with prior work, we find
that shot sensitivity is broadly present across metric-based few-shot learners,
but in contrast to prior work, larger neural architectures provide a degree of
built-in robustness to varying test shot. More importantly, a simple,
previously known but greatly overlooked class of approaches based on cosine
distance consistently and greatly improves robustness to shot variation, by
removing sensitivity to sample noise. We derive cosine alternatives to popular
and recent few-shot classifiers, broadening their applicability to realistic
settings. These cosine models consistently improve shot-robustness, outperform
prior shot-robust state of the art, and provide competitive accuracy on a range
of benchmarks and architectures, including notable gains in the very-low-shot
regime.

Computer Vision and Pattern Recognition;Artificial Intelligence;Machine Learning