Negative Margin Matters: Understanding Margin in Few-shot Classification

Understand

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.