Abstract Image attribute editing is a challenging problem that has been recently studied by many researchers using generative networks. The challenge is in the manipulation of selected attributes of images while preserving the other details. The method to achieve this goal is to find an accurate latent vector representation of an image and a direction corresponding to the attribute. Almost all the works in the literature use labeled datasets in a supervised setting for this purpose. In this study, we introduce an architecture called Cyclic Reverse Generator (CRG), which allows learning the inverse function of the generator accurately via an encoder in an unsupervised setting by utilizing cyclic cost minimization. Attribute editing is then performed using the CRG models for finding desired attribute representations in the latent space. In this work, we use two arbitrary reference images, with and without desired attributes, to compute an attribute direction for editing. We show that the proposed approach performs better in terms of image reconstruction compared to the existing end-to-end generative models both quantitatively and qualitatively. We demonstrate state-of-the-art results on both real images and generated images in CelebA dataset.

中文翻译：

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使用生成对抗网络的半监督图像属性编辑

摘要 图像属性编辑是一个具有挑战性的问题，最近许多研究人员使用生成网络进行了研究。挑战在于在保留其他细节的同时处理选定的图像属性。实现这一目标的方法是找到图像的准确潜在向量表示和属性对应的方向。为此，几乎所有文献中的作品都在监督设置中使用标记数据集。在这项研究中，我们引入了一种称为循环反向生成器 (CRG) 的架构，该架构允许通过利用循环成本最小化在无监督设置中通过编码器准确地学习生成器的反函数。然后使用 CRG 模型执行属性编辑，以在潜在空间中查找所需的属性表示。在这项工作中，我们使用两个任意参考图像，带有和不带有所需属性，来计算用于编辑的属性方向。我们表明，与现有的端到端生成模型相比，所提出的方法在图像重建方面在数量和质量上都表现更好。我们在 CelebA 数据集中的真实图像和生成图像上展示了最先进的结果。