Feature concatenation for adversarial domain adaptation

• Published in: Expert systems with applications Vol. 169; p. 114490
• Main Authors: Li, Jingyao, Li, Zhanshan, Lü, Shuai
• Format: Journal Article
• Language: English
• Published: New York Elsevier Ltd 01.05.2021; Elsevier BV
• Subjects: Adaptation; Adversarial learning; Domain adaptation; Feature concatenation; Feature extraction; Invariants; Representations; Domain adaptation; Adversarial learning; Feature concatenation
• ISSN: 0957-4174; 1873-6793
• DOI: 10.1016/j.eswa.2020.114490

Domain adaptation aims to mitigate the domain gap between the source and target domains so that knowledge can be transferred between domains. There are two key factors that determine the adaptation performance: transferability and discriminability. Transferability depends on the similarity of two domains. With transferability, the model learnt on the source domain can be used in the target domain. Discriminability indicates the separability of different classes. With discriminability, the adapted target features can be classified more accurately. Adversarial domain adaptation methods learn domain-invariant feature representations through adversarial learning. The domain-invariant feature representation guarantees the transferability. However, to obtain domain-invariant features, certain domain-specific information is suppressed, which may cause the loss of discriminability. To this end, we aim to enhance the discriminability by enriching the information contained in the domain-invariant features. We propose a Feature Concatenation for adversarial Domain Adaptation (FCDA) method. FCDA learns two feature extractors that can generate two different feature views for a sample. The concatenation of these two views is used as the feature representation of a sample, which we call the concatenation feature. Distribution alignment is performed on the concatenation features. We find that when the distributions of the concatenation features are aligned, the two feature views involved in a concatenation feature have different distributions. Thus, the concatenation feature contains more discriminative information, thereby enhancing the discriminative ability of the domain-invariant features. Experiments are carried out on four widely used datasets and FCDA exceeds some recent domain adaptation methods. •The proposed adversarial domain adaptation method enhances the discriminability.•The proposed method represents a sample by concatenating two different views.•The consistency and complementarity of two views are guaranteed in both domains.•The model is optimized in an adversarial way.