  We propose regression networks for the problem of few-shot classification,
where a classifier must generalize to new classes not seen in the training set,
given only a small number of examples of each class. In high dimensional
embedding spaces the direction of data generally contains richer information
than magnitude. Next to this, state-of-the-art few-shot metric methods that
compare distances with aggregated class representations, have shown superior
performance. Combining these two insights, we propose to meta-learn
classification of embedded points by regressing the closest approximation in
every class subspace while using the regression error as a distance metric.
Similarly to recent approaches for few-shot learning, regression networks
reflect a simple inductive bias that is beneficial in this limited-data regime
and they achieve excellent results, especially when more aggregate class
representations can be formed with multiple shots.
