Stochastic Adversarial Learning for Domain Adaptation

Jen-Tzung Chien, Ching Wei Huang

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


Learning across domains is challenging especially when test data in target domain are sparse, heterogeneous and unlabeled. This challenge is even severe when building a deep stochastic neural model. This paper presents a stochastic semi-supervised learning for domain adaptation by using labeled data from source domain and unlabeled data from target domain. There are twofold novelties in the proposed method. First, a graphical model is constructed to identify the random latent features for classes as well as domains which are learned by variational inference. Second, we learn the class features which are discriminative among classes and simultaneously invariant to both domains. An adversarial neural model is introduced to pursue domain invariance. The domain features are explicitly learned to purify the extraction of class features for an improved classification. The experiments on sentiment classification illustrate the merits of the proposed stochastic adversarial domain adaptation.

Original languageEnglish
Title of host publication2020 International Joint Conference on Neural Networks, IJCNN 2020 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728169262
StatePublished - Jul 2020
Event2020 International Joint Conference on Neural Networks, IJCNN 2020 - Virtual, Glasgow, United Kingdom
Duration: 19 Jul 202024 Jul 2020

Publication series

NameProceedings of the International Joint Conference on Neural Networks


Conference2020 International Joint Conference on Neural Networks, IJCNN 2020
CountryUnited Kingdom
CityVirtual, Glasgow


  • Adversarial learning
  • domain adaptation
  • sentiment classification
  • stochastic modeling

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