Image generating neural networks are mostly viewed as black boxes, where any change in the input can have a number of globally effective changes on the output. In this work, we propose a method for learning disentangled representations to allow for localized image manipulations. We use face images as our example of choice. Depending on the image region, identity and other facial attributes can be modified. The proposed network can transfer parts of a face such as shape and color of eyes, hair, mouth, etc.~directly between persons while all other parts of the face remain unchanged. The network allows to generate modified images which appear like realistic images. Our model learns disentangled representations by weak supervision. We propose a localized resnet autoencoder optimized using several loss functions including a loss based on the semantic segmentation, which we interpret as masks, and a loss which enforces disentanglement by decomposition of the latent space into statistically independent subspaces. We evaluate the proposed solution w.r.t. disentanglement and generated image quality. Convincing results are demonstrated using the CelebA dataset.

本论文提出了一种利用神经网络学习解开图像可控制性表示从而允许局部图像操作的方法，能够直接转移面部特定区域如眼睛、头发和嘴巴等部分的形状和颜色，而其他部位不变，通过使用定位的ResNet自编码器和几个损失函数进行训练，利用CelebA数据集检验了该方法的良好效果。

通过独立子空间学习分离的表示