Multi-source Domain Adaptation for Visual Sentiment Classification

• URL: http://arxiv.org/abs/2001.03886v1
• Date: Sun, 12 Jan 2020 08:37:42 GMT
• Title: Multi-source Domain Adaptation for Visual Sentiment Classification
• Authors: Chuang Lin, Sicheng Zhao, Lei Meng, Tat-Seng Chua
• Abstract summary: We propose a novel multi-source domain adaptation (MDA) method, termed Multi-source Sentiment Generative Adversarial Network (MSGAN) To handle data from multiple source domains, MSGAN learns to find a unified sentiment latent space where data from both the source and target domains share a similar distribution. Extensive experiments conducted on four benchmark datasets demonstrate that MSGAN significantly outperforms the state-of-the-art MDA approaches for visual sentiment classification.
• Score: 92.53780541232773
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Existing domain adaptation methods on visual sentiment classification typically are investigated under the single-source 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-the-art MDA approaches for visual sentiment classification.

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