  Few-shot learning features the capability of generalizing from a few
examples. In this paper, we first identify that a discriminative feature space,
namely a rectified metric space, that is learned to maintain the metric
consistency from training to testing, is an essential component to the success
of metric-based few-shot learning. Numerous analyses indicate that a simple
modification of the objective can yield substantial performance gains. The
resulting approach, called rectified metric propagation (ReMP), further
optimizes an attentive prototype propagation network, and applies a repulsive
force to make confident predictions. Extensive experiments demonstrate that the
proposed ReMP is effective and efficient, and outperforms the state of the arts
on various standard few-shot learning datasets.
