  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.
