  In order to mimic the human few-shot learning (FSL) ability better and to
make FSL closer to real-world applications, this paper proposes a practical FSL
(pFSL) setting. pFSL is based on unsupervised pretrained models (analogous to
human prior knowledge) and recognizes many novel classes simultaneously.
Compared to traditional FSL, pFSL is simpler in its formulation, easier to
evaluate, more challenging and more practical. To cope with the rarity of
training examples, this paper proposes IbM2, an instance-based max-margin
method not only for the new pFSL setting, but also works well in traditional
FSL scenarios. Based on the Gaussian Annulus Theorem, IbM2 converts random
noise applied to the instances into a mechanism to achieve maximum margin in
the many-way pFSL (or traditional FSL) recognition task. Experiments with
various self-supervised pretraining methods and diverse many- or few-way FSL
tasks show that IbM2 almost always leads to improvements compared to its
respective baseline methods, and in most cases the improvements are
significant. With both the new pFSL setting and novel IbM2 method, this paper
shows that practical few-shot learning is both viable and promising.
