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Transferable Adversarial Cycle Alignment for Domain Adaption

机译：领域适应性的可转移对抗周期对齐

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摘要

Domain adaption is definitely critical for success in bridging source and target domains that data distribution shifts exist in domain or task. The state-of-the-art of the adversarial feature learning model named Bidirectional Generative Adversarial Networks (BiGAN), forces generative models to align with an arbitrarily complex distribution in a latent space. However, BiGAN only matches single data distribution without exploiting multi-domain structure, which means the learned latent representation could not transfer to related target domains. Recent research has proved that GANs combined with Cycle Consistent Constraints are effective at image translation. Therefore, we propose a novel framework named Transferable Bidirectional Generative Adversarial Networks combining with Cycle-Consistent Constraints (Cycle-TBiGAN) be applied in cross-domain translation, which aims at learning an alignment latent feature representation and achieving a mapping function between domains. Our framework is suitable for a wide variety of domain adaption scenarios. We show the surprising results in the task of image translation without prior ground-truth knowledge. Extensive experiments are presented on several public datasets. Quantitative comparisons demonstrate the superiority of our approach against previous methods.
机译：域桥接对于成功桥接源域和目标域至关重要，因为在域或任务中存在数据分布转移。名为双向生成对抗网络（BiGAN）的对抗特征学习模型的最新技术，迫使生成模型与潜在空间中任意复杂的分布保持一致。但是，BiGAN仅匹配单个数据分配而没有利用多域结构，这意味着学习到的潜在表示无法转移到相关的目标域。最近的研究证明，GAN与循环一致性约束相结合可以有效地进行图像转换。因此，我们提出了一种新颖的框架，称为可转移双向生成对抗网络，结合了循环一致性约束（Cycle-TBiGAN）应用于跨域翻译，其目的是学习对齐潜在特征表示并实现域之间的映射功能。我们的框架适用于各种领域适应方案。我们在没有事先实地知识的情况下，在图像翻译任务中显示了令人惊讶的结果。在几个公共数据集上进行了广泛的实验。定量比较证明了我们的方法相对于先前方法的优越性。

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