Deep neural networks have dramatically advanced the state of the art for many
areas of machine learning. Recently they have been shown to have a remarkable
ability to generate highly complex visual artifacts such as images and text
rather than simply recognize them.
In this work we use neural networks to effectively invert low-dimensional
face embeddings while producing realistically looking consistent images. Our
contribution is twofold, first we show that a gradient ascent style approaches
can be used to reproduce consistent images, with a help of a guiding image.
Second, we demonstrate that we can train a separate neural network to
effectively solve the minimization problem in one pass, and generate images in
real-time. We then evaluate the loss imposed by using a neural network instead
of the gradient descent by comparing the final values of the minimized loss
function.

这篇论文利用深度神经网络逆向低维人脸嵌入并生成高质量真实的图像，探索了两种方法，通过引导图像使用梯度上升方法生成一致性良好的图像和通过训练单独的神经网络一次性解决最小化问题来实时生成图像，并对神经网络与梯度下降进行对比实验。