Few-Shot Learning With Embedded Class Models and Shot-Free Meta Training

We propose a method for learning embeddings for fewshot learning that is suitable for use with any number of shots (shot-free). Rather than fixing the class prototypes to be the Euclidean average of sample embeddings, we allow them to live in a higher-dimensional space (embedded class models) and learn the prototypes along with the model parameters. The class representation function is defined implicitly, which allows us to deal with a variable number of shots per class with a simple constant-size architecture. The class embedding encompasses metric learning, that facilitates adding new classes without crowding the class representation space. Despite being general and not tuned to the benchmark, our approach achieves state-of-the-art performance on the standard few-shot benchmark datasets. Figure 1 . One image of a mushroom (Muscaria) may be enough to recognize it in the wild (left); in other cases, there may be more subtle differences between an edible (Russula, shown in the center) and a deadly one (Phalloides, shown on the right), but still few samples are enough for humans.