在本文中，我們解決了多源域訓練的問題，目的是在沒有自适應步驟的情況下在測試時推廣到新的域。這就是所謂的域泛化(DG)。以前的DG工作假設相同的類别或标簽空間跨源域。在源域之間類别發生轉移的情況下，以往的DG方法由于标簽空間之間的不比對，容易出現負遷移，降低了目标的分類精度。為了解決上述問題，我們引入了端到端特征範數網絡(FNN)，該網絡不需要比對源域間的特征分布，對負遷移具有魯棒性。為了進一步提高特征範數網絡的泛化能力，本文還引入了一種協同特征範數網絡(CFNN)。CFNN比對每個訓練樣本下一個最可能類别的預測，增加了每個網絡的後驗熵。我們将所提出的模糊神經網絡和模糊神經網絡應用于DG的圖像分類問題，并證明了其顯著的改進。
原文題目：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.
基于特征範數網絡的深度域泛化.pdf