  This paper introduces a negative margin loss to metric learning based
few-shot learning methods. The negative margin loss significantly outperforms
regular softmax loss, and achieves state-of-the-art accuracy on three standard
few-shot classification benchmarks with few bells and whistles. These results
are contrary to the common practice in the metric learning field, that the
margin is zero or positive. To understand why the negative margin loss performs
well for the few-shot classification, we analyze the discriminability of
learned features w.r.t different margins for training and novel classes, both
empirically and theoretically. We find that although negative margin reduces
the feature discriminability for training classes, it may also avoid falsely
mapping samples of the same novel class to multiple peaks or clusters, and thus
benefit the discrimination of novel classes. Code is available at
https://github.com/bl0/negative-margin.few-shot.
