ISSN 1000-1239 CN 11-1777/TP

Journal of Computer Research and Development ›› 2021, Vol. 58 ›› Issue (9): 1997-2012.doi: 10.7544/issn1000-1239.2021.20200569

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Adversarial Discriminative Domain Adaptation Algorithm with CapsNet

Dai Hong, Sheng Lijie, Miao Qiguang   

  1. (College of Computer Science and Technology, Xidian University, Xi’an 710071) (Xi’an Key Laboratory of Big Data and Intelligent Vision (Xidian University), Xi’an 710071)
  • Online:2021-09-01
  • Supported by: 
    This work was supported by the National Natural Science Foundation of China (61772396, 61902296), the Project of Xi’an Key Laboratory of Big Data and Intelligent Vision (201805053ZD4CG37), and the China Postdoctoral Science Foundation (2019M663640).

Abstract: Recently, studies on domain adaptation have shown the effectiveness of adversarial learning in filling the differences between two domains, but there are still some limitations that samples taken from two domains are not enough to keep the domain invariance in potential spaces. Inspired by the fact that CapsNet(capsule network) has a strong ability to extract the invariance of features from samples, we introduce it into the domain adaptation problem. Firstly, a new convolution algorithm is devised over capsule-layer, combined with residual block, which makes it possible to train a deeper CapsNet. Results demonstrate that this new structure of CapsNet has a stronger ability to extract features. Secondly, the traditional adversarial discriminative adaptation methods have the defect that prones to blur the boundary between different domains, which in turn leads to a decline in the discriminative performance. Inspired by VAE-GAN(variational auto-encoder, generative adversarial networks), we use a reconstruction network as a strong constraint, so that the adversarial discriminative network avoids the inherent defect of mode collapse when the convolution base is transferring, and ensures that the discriminator is sensitive to the invariance of representation in different domains. Experiments on standard datasets show that our model achieves better performance in domain adaptation tasks of varying complexity.

Key words: capsule network (CapsNet), adversarial discriminative network, domain adaptation, generative adversarial networks, auto-encoder

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