重新思考生成对抗网络的编辑：一种基于降维的编辑向量估计方法
Rethinking the editing of generative adversarial networks: a method to estimate editing vectors based on dimension reduction
AI总结：
本文提出一种不依赖语义分割和可微网络的GAN编辑向量估计方法，通过改进LDA处理二元和连续特征编辑，在服装、肤色等非语义特征上表现良好。
摘要：
虽然生成对抗网络（GANs）最近在图像编辑中得到了应用，但大多数以前基于GAN的图像编辑方法需要带有语义分割标注的大规模数据集进行训练，仅提供高层次控制，或仅仅在不同图像之间进行插值。之前的研究者提出了EditGAN，通过寻找“编辑向量”来实现有限语义标注下的高质量、高精度语义图像编辑。然而，注意到存在许多与语义关联不高的特征，EditGAN在这些特征上可能会失败。基于EditGAN观察到的潜在空间的正交性，我们提出了一种估计编辑向量的方法，该方法不依赖于语义分割，也不依赖于可微的特征估计网络。我们的方法假设特征强度分布与隐藏向量分布之间存在相关性，并通过采样与几个隐藏向量对应的图像的特征强度来估计上述分布之间的关系。我们修改了线性判别分析（LDA）以处理二元特征编辑和连续特征编辑。然后我们发现，该方法在处理服装类型和纹理、肤色和头发等特征时效果良好。
英文摘要：
While Generative Adversarial Networks (GANs) have recently found applications in image editing, most previous GAN-based image editing methods require largescale datasets with semantic segmentation annotations for training, only provide high level control, or merely interpolate between different images. Previous researchers have proposed EditGAN for high-quality, high-precision semantic image editing with limited semantic annotations by finding `editing vectors'. However, it is noticed that there are many features that are not highly associated with semantics, and EditGAN may fail on them. Based on the orthogonality of latent space observed by EditGAN, we propose a method to estimate editing vectors that do not rely on semantic segmentation nor differentiable feature estimation network. Our method assumes that there is a correlation between the intensity distribution of features and the distribution of hidden vectors, and estimates the relationship between the above distributions by sampling the feature intensity of the image corresponding to several hidden vectors. We modified Linear Discriminant Analysis (LDA) to deal with both binary feature editing and continuous feature editing. We then found that this method has a good effect in processing features such as clothing type and texture, skin color and hair.
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