探索二元元学习以增强开放集场景中的领域泛化

arXiv:2606.23758v1 Announce Type: new Abstract: Domain generalization learns from multiple source domains to generalize to unseen target domains.然而，它经常忽略源和目标之间标签不匹配的实际情况。然后提出开放集域泛化来识别未见域中的未见类。一种简单的方法训练一对多分类器来分离每个类别并将异常值检测为未知。 Yet, the imbalance between few positive samples and many negative samples skews the decision boundary towards the positive ones, leading the model to over-reject out-of-distribution data, even from known classes in unseen domains.在本文中，我们提出了一种新颖的元学习策略，称为具有联合域内类匹配（MEDIC）的二元元学习，它同时考虑对域间和类间任务分割的隐式梯度匹配，以找到域和类之间平衡的最佳边界。实验结果表明，MEDIC不仅在开放集场景中优于现有方法，而且还保持了有竞争力的封闭集泛化能力。