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.

本文提出了一种多源领域自适应方法，称为多源情感生成对抗网络（MSGAN），用于视觉情感分类，通过循环一致性对抗性学习以端到端的方式，MSGAN 可以学习找到一个统一的情感潜在空间，其中来自源域和目标域的数据共享类似的分布。对四个基准数据集进行的广泛实验表明，MSGAN 在视觉情感分类方面明显优于最先进的多源领域自适应方法。