扭曲的GAN：无配对数据下的语义属性编辑
The GAN that Warped: Semantic Attribute Editing with Unpaired Data
AI总结：
本文提出通过平滑扭曲场进行语义图像编辑，利用生成对抗网络在无配对数据下实现高分辨率人脸编辑，有效保持身份特征。
摘要：
深度神经网络近年来在图像编辑中取得显著成功，尤其在人脸领域。然而，它们通常受限于有限的分辨率范围。许多方法过于灵活，导致人脸编辑时可能丢失身份特征。本文提出通过平滑扭曲场学习语义图像编辑，利用生成对抗网络在无配对数据下训练模型。我们通过单次深度网络前向传递在低分辨率下实现极高分辨率（4k）的人脸编辑，并展示编辑结果在保持身份方面显著优于现有方法。通过在Cub200鸟类数据集上展示合理图像编辑结果，证明了方法的鲁棒性。据我们所知，此工作首次在具有挑战性的数据集上实现了无配对数据下的语义属性编辑。
英文摘要：
Deep neural networks have recently been used to edit images with great success, in particular for faces. However, they are often limited to only being able to work at a restricted range of resolutions. Many methods are so flexible that face edits can often result in an unwanted loss of identity. This work proposes to learn how to perform semantic image edits through the application of smooth warp fields. Previous approaches that attempted to use warping for semantic edits required paired data, i.e. example images of the same subject with different semantic attributes. In contrast, we employ recent advances in Generative Adversarial Networks that allow our model to be trained with unpaired data. We demonstrate face editing at very high resolutions (4k images) with a single forward pass of a deep network at a lower resolution. We also show that our edits are substantially better at preserving the subject's identity. The robustness of our approach is demonstrated by showing plausible image editing results on the Cub200 birds dataset. To our knowledge this has not been previously accomplished, due the challenging nature of the dataset.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
