CyCADA: Cycle-Consistent Adversarial Domain Adaptation-猫眼课题宝

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发表时间：

2017-11

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ArXiv

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其他

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作者：

Judy Hoffman;Eric Tzeng;Taesung Park;Jun-Yan Zhu;Phillip Isola;Kate Saenko;Alexei A. Efros;Trevor Darrel

域的适应对于在新的，看不见的环境中的成功至关重要。在特征空间中应用的对抗性适应模型会发现域不变表示，但很难可视化，有时无法捕获像素级和低级域移动。最近的工作表明，即使没有使用对齐的图像对，即使没有使用对齐的图像对，也可以在域之间映射图像，在映射域之间的图像方面也非常有效。我们提出了一种新型的歧视训练循环循环抗对抗结构域的适应模型。 Cycada在像素级和特征级别上都适应了表示，在利用任务损失的同时执行周期矛盾，并且不需要对齐对。我们的模型可以应用于各种视觉识别和预测设置。我们在多个适应任务中展示了新的最新结果，包括数字分类和道路场景的语义分割，证明了从合成世界到现实世界域的转移。

Domain adaptation is critical for success in new, unseen environments. Adversarial adaptation models applied in feature spaces discover domain invariant representations, but are difficult to visualize and sometimes fail to capture pixel-level and low-level domain shifts. Recent work has shown that generative adversarial networks combined with cycle-consistency constraints are surprisingly effective at mapping images between domains, even without the use of aligned image pairs. We propose a novel discriminatively-trained Cycle-Consistent Adversarial Domain Adaptation model. CyCADA adapts representations at both the pixel-level and feature-level, enforces cycle-consistency while leveraging a task loss, and does not require aligned pairs. Our model can be applied in a variety of visual recognition and prediction settings. We show new state-of-the-art results across multiple adaptation tasks, including digit classification and semantic segmentation of road scenes demonstrating transfer from synthetic to real world domains.

CyCADA: Cycle-Consistent Adversarial Domain Adaptation

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