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Computer Science > Machine Learning
Title: Hacking Task Confounder in Meta-Learning
(Submitted on 10 Dec 2023 (v1), last revised 18 Apr 2024 (this version, v3))
Abstract: Meta-learning enables rapid generalization to new tasks by learning knowledge from various tasks. It is intuitively assumed that as the training progresses, a model will acquire richer knowledge, leading to better generalization performance. However, our experiments reveal an unexpected result: there is negative knowledge transfer between tasks, affecting generalization performance. To explain this phenomenon, we conduct Structural Causal Models (SCMs) 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 these findings, we propose a plug-and-play Meta-learning Causal Representation Learner (MetaCRL) to eliminate task confounders. It encodes decoupled generating 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.
Submission history
From: Jingyao Wang [view email][v1] Sun, 10 Dec 2023 05:33:40 GMT (304kb,D)
[v2] Wed, 17 Apr 2024 08:36:14 GMT (2722kb,D)
[v3] Thu, 18 Apr 2024 13:31:28 GMT (2722kb,D)
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