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Deep neural networks have dramatically advanced the state of the art for manyareas of machine learning. Recently they have been shown to have a remarkableability to generate highly complex visual artifacts such as images and textrather than simply recognize them.  In this work we use neural networks to effectively invert low-dimensionalface embeddings while producing realistically looking consistent images. Ourcontribution is twofold, first we show that a gradient ascent style approachescan be used to reproduce consistent images, with a help of a guiding image.Second, we demonstrate that we can train a separate neural network toeffectively solve the minimization problem in one pass, and generate images inreal-time. We then evaluate the loss imposed by using a neural network insteadof the gradient descent by comparing the final values of the minimized lossfunction.

Inverting face embeddings with convolutional neural networks