Generating Images with Perceptual Similarity Metrics based on Deep Networks Generating Images with Perceptual Similarity Metrics based on Deep Networks
Paper summary This paper proposed a class of loss functions applicable to image generation that are based on distance in feature spaces: $$\mathcal{L} = \lambda_{feat}\mathcal{L}_{feat} + \lambda_{adv}\mathcal{L}_{adv} + \lambda_{img}\mathcal{L}_{img}$$ ### Key Points - Using only l2 loss in image space yields over-smoothed results since it leads to averaging all likely locations of details. - L_feat measures the distance in suitable feature space and therefore preserves distribution of fine details instead of exact locations. - Using only L_feat yields bad results since feature representations are contractive. Many non-natural images also mapped to the same feature vector. - By introducing a natural image prior - GAN, we can make sure that samples lie on the natural image manifold. ### Model https://i.imgur.com/qNzMwQ6.png ### Exp - Training Autoencoder - Generate images using VAE - Invert feature ### Thought I think the experiment section is a little complicated to comprehend. However, the proposed loss seems really promising and can be applied to many tasks related to image generation. ### Questions - Section 4.2 & 4.3 are hard to follow for me, need to pay more attention in the future
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Generating Images with Perceptual Similarity Metrics based on Deep Networks
Alexey Dosovitskiy and Thomas Brox
arXiv e-Print archive - 2016 via Local arXiv
Keywords: cs.LG, cs.CV, cs.NE

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