论文标题
使用潜在的对抗发电机创建高分辨率图像
Creating High Resolution Images with a Latent Adversarial Generator
论文作者
论文摘要
生成逼真的图像很困难，最近已经提出了许多针对此任务的配方。但是，如果将任务限制为生成特定图像类别的任务，则该任务变得更加可行。也就是说，我们没有从自然图像的多种图像中生成任意图像作为样本，而是提议从自然图像的特定“子空间”中采样图像，该图像由同一子空间的低分辨率图像导演。我们解决的问题虽然接近单像超级分辨率问题的表述，但实际上是不同的。单图像超分辨率是从相对较低的分辨率图像中预测最接近地面真相的图像的任务。我们建议使用一种称为潜在对抗发电机（LAG）的新方法，生成高分辨率图像的样品。在我们的生成采样框架中，我们仅使用输入（可能非常低分辨率）来指导网络应产生的样本类别。因此，我们的算法的输出不是与输入有关的唯一图像，而是从自然图像的歧管中采样的相关图像的可能se}。我们的方法使用感知损失仅在对手的潜在空间中学习 - 它没有像素损失。
Generating realistic images is difficult, and many formulations for this task have been proposed recently. If we restrict the task to that of generating a particular class of images, however, the task becomes more tractable. That is to say, instead of generating an arbitrary image as a sample from the manifold of natural images, we propose to sample images from a particular "subspace" of natural images, directed by a low-resolution image from the same subspace. The problem we address, while close to the formulation of the single-image super-resolution problem, is in fact rather different. Single image super-resolution is the task of predicting the image closest to the ground truth from a relatively low resolution image. We propose to produce samples of high resolution images given extremely small inputs with a new method called Latent Adversarial Generator (LAG). In our generative sampling framework, we only use the input (possibly of very low-resolution) to direct what class of samples the network should produce. As such, the output of our algorithm is not a unique image that relates to the input, but rather a possible se} of related images sampled from the manifold of natural images. Our method learns exclusively in the latent space of the adversary using perceptual loss -- it does not have a pixel loss.
