Multi-Source Domain Adaptation for Visual Sentiment Classification

Existing domain adaptation methods on visual sentiment classification typically are investigated under the singlesource scenario, where the knowledge learned from a source domain of sufficient labeled data is transferred to the target domain of loosely labeled or unlabeled data. However, in practice, data from a single source domain usually have a limited volume and can hardly cover the characteristics of the target domain. In this paper, we propose a novel multi-source domain adaptation (MDA) method, termed Multi-source Sentiment Generative Adversarial Network (MSGAN), for visual sentiment classification. To handle data from multiple source domains, it learns to find a unified sentiment latent space where data from both the source and target domains share a similar distribution. This is achieved via cycle consistent adversarial learning in an end-to-end manner. Extensive experiments conducted on four benchmark datasets demonstrate that MSGAN significantly outperforms the state-of-theart MDA approaches for visual sentiment classification.

Authors

• Chuang Lin
• Sicheng Zhao Tsinghua University
• Lei Meng School of Software, Shandong University, Jinan, China Shandong Research Institute of Industrial Technology, Jinan, China
• Tat-Seng Chua National University of Singapore

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