We uncover an ever-overlooked deficiency in the prevailing Few-Shot Learning
(FSL) methods: the pre-trained knowledge is indeed a confounder that limits the
performance. This finding is rooted from our causal assumption: a Structural
Causal Model (SCM) for the causalities among the pre-trained knowledge, sample
features, and labels. Thanks to it, we propose a novel FSL paradigm:
Interventional Few-Shot Learning (IFSL). Specifically, we develop three
effective IFSL algorithmic implementations based on the backdoor adjustment,
which is essentially a causal intervention towards the SCM of many-shot
learning: the upper-bound of FSL in a causal view. It is worth noting that the
contribution of IFSL is orthogonal to existing fine-tuning and meta-learning
based FSL methods, hence IFSL can improve all of them, achieving a new
1-/5-shot state-of-the-art on \textit{mini}ImageNet, \textit{tiered}ImageNet,
and cross-domain CUB. Code is released at this https URL

通过构建因果模型，提出了一种新的干预式 Few-Shot 学习范式，与现有的 Fine-tuning 和 Meta-learning 等方法正交，可以显著提高它们的性能，在多个数据集上均取得新的 1-/5-shot 最新成果。