  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.
