Generating Images with Perceptual Similarity Metrics based on Deep Networks

Part of Advances in Neural Information Processing Systems 29 (NIPS 2016)

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Alexey Dosovitskiy, Thomas Brox


We propose a class of loss functions, which we call deep perceptual similarity metrics (DeePSiM), allowing to generate sharp high resolution images from compressed abstract representations. Instead of computing distances in the image space, we compute distances between image features extracted by deep neural networks. This metric reflects perceptual similarity of images much better and, thus, leads to better results. We demonstrate two examples of use cases of the proposed loss: (1) networks that invert the AlexNet convolutional network; (2) a modified version of a variational autoencoder that generates realistic high-resolution random images.