Adversarial Divergence Training for Universal Cross-Scene Classification

Cross-scene classification has recently gained increasing interest, which improves the classification performance on label-scarce domains by transferring knowledge learned from label-rich domains. Domain adaptation (DA) attempts to solve the domain gap problem and it is widely used in the cross-scene applications. The priori knowledge that the label space between the source and target domains is identical is a key requirement for existing cross-scene approaches to work successfully. However, label sets of different domains can always be different in real applications, that is to say, there will be common as well as private categories for different domains. Universal DA (UniDA) has been proposed to deal with the above difficulty by relaxing all constraints on the label sets. In order to complete more general remote sensing cross-scene classification tasks regardless of label sets, we propose a UniDA cross-scene classification approach, adversarial divergence training (ADT), to simultaneously classify the target common categories and detect the target private categories based on the divergence of different classifiers. ADT attempts to train the classifier and feature extractor against each other (in adversarial) in order to extract more domain-invariant and discriminative features. At the same time, divergence optimization of different classifiers is used to distinguish the target private class. The former makes it capable of cross-scene tasks, while the latter weakens the effect of the label set on the performance of the algorithm. Experiments show that ADT outperforms baselines in the UniDA setting and even in other settings.