Revisiting Few-Shot Learning From a Causal Perspective

• Published in: IEEE transactions on knowledge and data engineering Vol. 36; no. 11; pp. 6908 - 6919
• Main Authors: Lin, Guoliang, Xu, Yongheng, Lai, Hanjiang, Yin, Jian
• Format: Journal Article
• Language: English
• Published: IEEE 01.11.2024
• Subjects: Birds; causal inference; Cause effect analysis; Computational modeling; Correlation; Feature extraction; Few-shot learning; pre-trained vision-language model; Task analysis; Training
• ISSN: 1041-4347; 1558-2191
• DOI: 10.1109/TKDE.2024.3397689

Few-shot learning with <inline-formula><tex-math notation="LaTeX">N</tex-math> <mml:math><mml:mi>N</mml:mi></mml:math><inline-graphic xlink:href="lin-ieq1-3397689.gif"/> </inline-formula>-way <inline-formula><tex-math notation="LaTeX">K</tex-math> <mml:math><mml:mi>K</mml:mi></mml:math><inline-graphic xlink:href="lin-ieq2-3397689.gif"/> </inline-formula>-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.