TY - JOUR AU - Lin, Chuang AU - Zhao, Sicheng AU - Meng, Lei AU - Chua, Tat-Seng PY - 2020/04/03 Y2 - 2024/03/28 TI - Multi-Source Domain Adaptation for Visual Sentiment Classification JF - Proceedings of the AAAI Conference on Artificial Intelligence JA - AAAI VL - 34 IS - 03 SE - AAAI Technical Track: Humans and AI DO - 10.1609/aaai.v34i03.5651 UR - https://ojs.aaai.org/index.php/AAAI/article/view/5651 SP - 2661-2668 AB - <p>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.</p> ER -