Few-Shot Learning with Embedded Class Models and Shot-Free Meta Training
We propose a method for learning embeddings for few-shot 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.