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We propose a class of loss functions, which we call deep\nperceptual similarity metrics (DeePSiM), that mitigate this problem. Instead of\ncomputing distances in the image space, we compute distances between image\nfeatures extracted by deep neural networks. This metric better reflects\nperceptually similarity of images and thus leads to better results. We show\nthree applications: autoencoder training, a modification of a variational\nautoencoder, and inversion of deep convolutional networks. 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