在本文中，我们解决了多源域训练的问题，目的是在没有自适应步骤的情况下在测试时推广到新的域。这就是所谓的域泛化(DG)。以前的DG工作假设相同的类别或标签空间跨源域。在源域之间类别发生转移的情况下，以往的DG方法由于标签空间之间的不匹配，容易出现负迁移，降低了目标的分类精度。为了解决上述问题，我们引入了端到端特征范数网络(FNN)，该网络不需要匹配源域间的特征分布，对负迁移具有鲁棒性。为了进一步提高特征范数网络的泛化能力，本文还引入了一种协同特征范数网络(CFNN)。CFNN匹配每个训练样本下一个最可能类别的预测，增加了每个网络的后验熵。我们将所提出的模糊神经网络和模糊神经网络应用于DG的图像分类问题，并证明了其显著的改进。
原文题目：Deep Domain Generalization with Feature-norm Network
原文：In this paper, we tackle the problem of training with multiple source domains with the aim to generalize to new domains at test time without an adaptation step. This is known as domain generalization (DG). Previous works on DG assume identical categories or label space across the source domains. In the case of category shift among the source domains, previous methods on DG are vulnerable to negative transfer due to the large mismatch among label spaces, decreasing the target classification accuracy. To tackle the aforementioned problem, we introduce an end-to-end feature-norm network (FNN) which is robust to negative transfer as it does not need to match the feature distribution among the source domains. We also introduce a collaborative feature-norm network (CFNN) to further improve the generalization capability of FNN. The CFNN matches the predictions of the next most likely categories for each training sample which increases each network's posterior entropy. We apply the proposed FNN and CFNN networks to the problem of DG for image classification tasks and demonstrate significant improvement over the state-of-the-art.
基于特征范数网络的深度域泛化.pdf