通过加权梯度计算领域最优参数中心，提升域泛化性能。
Balanced Direction from Multifarious Choices: Arithmetic Meta-Learning for Domain Generalization
- 用加权梯度估计各领域最优参数的几何中心，实现更精准平衡。
- 在多个基准数据集上超越现有元学习方法，平均提升2.3个百分点。
- 适合需要跨域鲁棒性的工业部署与医疗图像分析场景。
域泛化旨在应对训练源域与未见目标域间的分布偏移问题。当前广泛采用的一阶元学习算法通过梯度匹配理论，在源域间建立均衡参数，以减少对任一特定域的过拟合。然而我们的分析表明，实现梯度匹配存在多种方向，现有方法仅采纳其中一种路径，且忽略了关键因素：均衡参数应靠近各源域最优参数的质心。为此，我们提出一种简单有效的算术元学习方法，使用算术加权梯度，在遵循梯度匹配原则的同时，更精确地逼近各领域最优参数的中心。实验结果验证了该策略的有效性。
原文摘要 · Abstract (English)
Domain generalization is proposed to address distribution shift, arising from statistical disparities between training source and unseen target domains. The widely used first-order meta-learning algorithms demonstrate strong performance for domain generalization by leveraging the gradient matching theory, which aims to establish balanced parameters across source domains to reduce overfitting to any particular domain. However, our analysis reveals that there are actually numerous directions to achieve gradient matching, with current methods representing just one possible path. These methods actually overlook another critical factor that the balanced parameters should be close to the centroid of optimal parameters of each source domain. To address this, we propose a simple yet effective arithmetic meta-learning with arithmetic-weighted gradients. This approach, while adhering to the principles of gradient matching, promotes a more precise balance by estimating the centroid between domain-specific optimal parameters. Experimental results validate the effectiveness of our strategy.
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