Jisuanji kexue yu tansuo (May 2022)
Deep Adversarial-Reconstruction-Classification Networks for Unsupervised Domain Adaptation
Abstract
Recently, a new method of transfer learning called adversarial domain adaptation, embeds the idea of the generative adversarial networks (GAN) into the deep networks. It can learn the transferable representation of data for domain adaptation by the thought of the GAN. Although this method can extract the common features of the source domain data and target domain data, and effectively transfer knowledge between different domains, the existing adversarial domain adaptation algorithms cannot effectively retain the local features of the target domain. However, some features of the target domain data may significantly improve the classification accuracy. In order to avoid the destruction of the local features of the original data due to adversarial learning, a multi-task neural network is used to retain the local features of the target domain data. A model of deep adversarial-reconstruction-classification networks (DARCN) is proposed. DARCN is inspired by the auto-encoder. On the basis of adversarial domain adap-tation, the decoding part of the auto-encoder is added, which can effectively reconstruct the original data from low-dimensional features. The model learns shared coding representations for the following tasks: supervised classification of labeled source domain data, unsupervised reconstruction of unlabeled target domain data and indistinguishability of source domain and target domain. Finally, the classification loss of the label classifier and the reconstruction loss of the decoder are minimized, and the classification loss of the domain discriminator is maximized. The gradient descent method can effectively solve such optimization problems. The experimental results prove that the preservation of local features of target domain is critical for domain adaptation tasks.
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