Revisiting Few-Shot Learning from a Causal Perspective
arxiv(2022)
摘要
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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