Learning interpretable data representation has been an active research topic in deep learning and computer vision. While representation disentanglement is an effective technique for addressing this task, existing works cannot easily handle the problems in which manipulating and recognizing data across multiple domains are desirable. In this paper, we present a unified network architecture of Multi-domain and Multi-modal Representation Disentangler (M2RD), with the goal of learning domain-invariant content representation with the associated domain-specific representation observed. By advancing adversarial learning and disentanglement techniques, the proposed model is able to perform continuous image manipulation across data domains with multiple modalities. More importantly, the resulting domain-invariant feature representation can be applied for unsupervised domain adaptation. Finally, our quantitative and qualitative results would confirm the effectiveness and robustness of the proposed model over state-of-the-art methods on the above tasks.

中文翻译：

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用于跨域图像处理和分类的多域多模态表示分解器。

学习可解释的数据表示形式一直是深度学习和计算机视觉中的活跃研究主题。尽管表示纠缠是解决此任务的有效技术，但现有作品无法轻松处理需要跨多个域操纵和识别数据的问题。在本文中，我们提出了一个统一的多域和多模式表示Disentangler（M2RD）的网络体系结构，其目的是学习带有相关领域特定表示的领域不变内容表示。通过推进对抗性学习和解纠缠技术，提出的模型能够跨多种模式跨数据域执行连续图像处理。更重要的是，所得到的域不变特征表示可以应用于无监督域自适应。最后，我们的定量和定性结果将证实所提出模型相对于上述任务的最新方法的有效性和鲁棒性。