Unsupervised Domain Adaptation via Discriminative Classes-Center Feature Learning in Adversarial Network

Adversarial learning has achieved remarkable advance in learning transferable representations across different domains. Generally, previous works are mainly devoted to reducing domain shift between labeled source data and unlabeled target data by extracting domain-invariant features. However, these adversarial methods rarely consider task-specific decision boundaries among classes, causing classification performance degradation in cross domain tasks. In this paper, we propose a novel approach for the task of unsupervised domain adaptation via discriminative classes-center feature learning in adversarial network (C2FAN), which concentrates on learning domain-invariant representation and paying close attention to classification decision boundary simultaneously to improve the ability of transferable knowledge across different domains. C2FAN consists of a feature extractor, a classifier and a discriminator. Firstly, for reducing domain gaps between source and target domains in the feature extractor, we propose to utilize a conditional adversarial learning module to extract domain-invariant feature and improve discriminability of the classifier simultaneously. Further, we present a high-efficiency layer normalization module to reduce domain shift existing in the classifier. Secondly, we design a discriminative classes-center feature learning module in the classifier to diminish the distribution distance of the same-class samples so that the decision boundary can distinguish different classes easily, which can reduce the misclassification on target samples. What’s more, C2FAN is an effective yet considerable simple approach which can be embedded into current domain adaptation approaches conveniently. Extensive experiments demonstrate that our proposed model achieves satisfactory results on some standard domain adaptation benchmarks.