Meta-learning enables rapid generalization to new tasks by learning meta-knowledge from a variety of tasks. It is intuitively assumed that the more tasks a model learns in one training batch, the richer knowledge it acquires, leading to better generalization performance. However, contrary to this intuition, our experiments reveal an unexpected result: adding more tasks within a single batch actually degrades the generalization performance. To explain this unexpected phenomenon, we conduct a Structural Causal Model (SCM) for causal analysis. Our investigation uncovers the presence of spurious correlations between task-specific causal factors and labels in meta-learning. Furthermore, the confounding factors differ across different batches. We refer to these confounding factors as ``Task Confounders". Based on this insight, we propose a plug-and-play Meta-learning Causal Representation Learner (MetaCRL) to eliminate task confounders. It encodes decoupled causal factors from multiple tasks and utilizes an invariant-based bi-level optimization mechanism to ensure their causality for meta-learning. Extensive experiments on various benchmark datasets demonstrate that our work achieves state-of-the-art (SOTA) performance.

翻译：元学习通过从多种任务中学习元知识，能够快速泛化到新任务。直觉上认为，模型在单个训练批次中学习的任务越多，获取的知识就越丰富，从而获得更好的泛化性能。然而，与这一直觉相反，我们的实验揭示了一个出乎意料的结果：在单个批次中增加更多任务实际上会降低泛化性能。为解释这一反常现象，我们构建了结构因果模型（SCM）进行因果分析。研究发现，元学习中任务特定的因果因素与标签之间存在虚假相关性。此外，不同批次间的混淆因素也存在差异。我们将这些混淆因素称为"任务混淆因素"。基于这一发现，我们提出了一种即插即用的元学习因果表征学习器（MetaCRL），用于消除任务混淆因素。该方法从多个任务中编码解耦的因果因素，并利用基于不变量的双层优化机制确保其对元学习的因果性。在多个基准数据集上的大量实验表明，我们的工作达到了最先进的（SOTA）性能。