Abstract Domain adaptation is a significant and popular issue of solving distribution discrepancy among different domains in computer vision. Generally, previous works proposed are mainly devoted to reducing domain shift between source domain with labeled data and target domain without labels. Adversarial learning in deep networks has already been widely applied to learn disentangled and transferable features between two different domains to minimize domains distribution discrepancy. However, these methods rarely consider class distributions among source data during adversarial learning, and they pay little attention to these transferable regions among source and target domains images. In this paper, we propose a Generative Attention Adversarial Classification Network (GAACN) model for unsupervised domain adaptation. To learn a joint feature distribution between source and target domains, we present an improved generative adversarial network (GAN) following the feature extractor. Firstly, the discriminator of GAN discriminates the distribution of domains and the classes distribution among source data during adversarial learning, so that our feature extractor can learn a joint feature distribution between source and target domains and maintain the classes consistent simultaneously. Secondly, we present an attention module embedded in GAN, which allows the discriminator to discriminate the transferable regions among the images of source and target domains. Lastly, we propose a simple and efficient method which allocates pseudo-labels for unlabeled target data, and it can improve the performance of our model GAACN while mitigating negative transfer. Extensive experiments demonstrate that our proposed model achieves perfect results on several standard domain adaptation datasets.

中文翻译：

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用于无监督域适应的生成注意对抗分类网络

摘要 域适应是解决计算机视觉中不同域之间分布差异的重要且流行的问题。一般来说，以前提出的工作主要致力于减少有标记数据的源域和没有标记的目标域之间的域转移。深度网络中的对抗性学习已经被广泛应用于学习两个不同域之间的解缠结和可转移特征，以最大限度地减少域分布差异。然而，这些方法在对抗性学习过程中很少考虑源数据之间的类分布，并且很少关注源域和目标域图像之间的这些可转移区域。在本文中，我们提出了一种用于无监督域适应的生成注意对抗分类网络（GAACN）模型。为了学习源域和目标域之间的联合特征分布，我们在特征提取器之后提出了一个改进的生成对抗网络（GAN）。首先，GAN 的鉴别器在对抗性学习过程中区分域的分布和源数据之间的类分布，以便我们的特征提取器可以学习源域和目标域之间的联合特征分布，同时保持类的一致性。其次，我们提出了一个嵌入在 GAN 中的注意力模块，它允许鉴别器区分源域和目标域的图像之间的可转移区域。最后，我们提出了一种简单有效的方法，为未标记的目标数据分配伪标签，它可以在减少负迁移的同时提高我们模型 GAACN 的性能。