Classifiers trained on given databases perform poorly when tested on data acquired in different settings. This is explained in domain adaptation through a shift among distributions of the source and target domains. Attempts to align them have traditionally resulted in works reducing the domain shift by introducing appropriate loss terms, measuring the discrepancies between source and target distributions, in the objective function. Here we take a different route, proposing to align the learned representations by embedding in any given network specific Domain Alignment Layers, designed to match the source and target feature distributions to a reference one. Opposite to previous works which define a priori in which layers adaptation should be performed, our method is able to automatically learn the degree of feature alignment required at different levels of the deep network. Thorough experiments on different public benchmarks, in the unsupervised setting, confirm the power of our approach.

该论文介绍了一种基于领域适应方法的特征对齐技术，在深度神经网络中嵌入特定的领域对齐层，实现源特征分布和目标特征分布与参考特征分布的匹配，以提高不同领域数据分类的准确性。通过实验证明此方法的有效性。

AutoDIAL: 自动域对齐层