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arXiv · 2209.13816

Revisiting Few-Shot Learning from a Causal Perspective

Guoliang Lin·Yongheng Xu·Hanjiang Lai·Jian Yin
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Abstract

Few-shot learning with $N$-way $K$-shot scheme is an open challenge in machine learning. Many metric-based approaches have been proposed to tackle this problem, e.g., the Matching Networks and CLIP-Adapter. Despite that these approaches have shown significant progress, the mechanism of why these methods succeed has not been well explored. In this paper, we try to interpret these metric-based few-shot learning methods via causal mechanism. We show that the existing approaches can be viewed as specific forms of front-door adjustment, which can alleviate the effect of spurious correlations and thus learn the causality. This causal interpretation could provide us a new perspective to better understand these existing metric-based methods. Further, based on this causal interpretation, we simply introduce two causal methods for metric-based few-shot learning, which considers not only the relationship between examples but also the diversity of representations. Experimental results demonstrate the superiority of our proposed methods in few-shot classification on various benchmark datasets. Code is available in https://github.com/lingl1024/causalFewShot.

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Guoliang Lin, Yongheng Xu, Hanjiang Lai, Jian Yin. 2022-09-28. Revisiting Few-Shot Learning from a Causal Perspective. https://arxiv.org/abs/2209.13816

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