Negative Margin Matters: Understanding Margin in Few-shot Classification

european conference on computer vision, pp. 438-455, 2020.

Cited by: 6|Bibtex|Views66|DOI:https://doi.org/10.1007/978-3-030-58548-8_26
Other Links: arxiv.org|academic.microsoft.com

Abstract:

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...More

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