Confounders in deep learning are in general detrimental to model's generalization where they infiltrate feature representations. Therefore, learning causal features that are free of interference from confounders is important. Most previous causal learning based approaches employ back-door criterion to mitigate the adverse effect of certain specific confounder, which require the explicit identification of confounder. However, in real scenarios, confounders are typically diverse and difficult to be identified. In this paper, we propose a novel Confounder Identification-free Causal Visual Feature Learning (CICF) method, which obviates the need for identifying confounders. CICF models the interventions among different samples based on front-door criterion, and then approximates the global-scope intervening effect upon the instance-level interventions from the perspective of optimization. In this way, we aim to find a reliable optimization direction, which avoids the intervening effects of confounders, to learn causal features. Furthermore, we uncover the relation between CICF and the popular meta-learning strategy MAML, and provide an interpretation of why MAML works from the theoretical perspective of causal learning for the first time. Thanks to the effective learning of causal features, our CICF enables models to have superior generalization capability. Extensive experiments on domain generalization benchmark datasets demonstrate the effectiveness of our CICF, which achieves the state-of-the-art performance.
翻译:深层学习的创始人一般都有害于模型的概括性,因为他们渗透到特征代表处。因此,学习没有受混乱者干扰的因果特征非常重要。以前大多数基于因果关系的学习方法都采用后门标准来减轻某些特定混淆者的不利影响,这要求明确识别混淆者。然而,在真实的情景中,混淆者通常具有多样性,难以识别因果关系特征。在本文中,我们建议采用一个新的、没有身份识别者身份识别特征的无因果关系学习(CICF)方法,这样就不需要识别相形见绌者。CICF以前门标准为不同样本之间的干预模型模型,然后从优化的角度接近对实例一级干预产生的全球范围影响。通过这种方法,我们的目标是找到可靠的优化方向,避免混淆者的影响,了解因果关系特征。此外,我们发现CICF与流行的元学习战略(MCF)之间的关系,并解释MAML为何首次从因果关系学习的理论角度出发工作。由于有效学习了因果特征,因此,CICCF数据库能够实现总体性能率模型。