渐变器网络：通过滑动属性操纵图像
Fader Networks: Manipulating Images by Sliding Attributes
摘要：
本文介绍了一种新的编码器-解码器架构，该架构通过将图像的显着信息和属性值直接在潜在空间中进行分解来训练以重建图像。因此，经过训练后，我们的模型可以通过改变属性值来生成输入图像的不同真实版本。通过使用连续属性值，我们可以选择在生成的图像中可感知的特定属性的程度。这个属性可以允许用户使用滑动旋钮（如混音控制台上的推子）修改图像的应用程序，以改变肖像的面部表情，或更新某些对象的颜色。与主要依赖于通过在训练时改变属性值来在像素空间中训练对抗网络的最新技术相比，我们的方法产生了更简单的训练方案，并且可以很好地扩展到多个属性。我们提出的证据表明，我们的模型可以显着改变感知值的属性，同时保持图像的自然性。
英文摘要：
This paper introduces a new encoder-decoder architecture that is trained to reconstruct images by disentangling the salient information of the image and the values of attributes directly in the latent space. As a result, after training, our model can generate different realistic versions of an input image by varying the attribute values. By using continuous attribute values, we can choose how much a specific attribute is perceivable in the generated image. This property could allow for applications where users can modify an image using sliding knobs, like faders on a mixing console, to change the facial expression of a portrait, or to update the color of some objects. Compared to the state-of-the-art which mostly relies on training adversarial networks in pixel space by altering attribute values at train time, our approach results in much simpler training schemes and nicely scales to multiple attributes. We present evidence that our model can significantly change the perceived value of the attributes while preserving the naturalness of images.
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。
