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To train Variational Autoencoders (VAEs) to generate realistic imagery requires a loss function that reflects human perception of image similarity.
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Digital watermarking and steganography
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Squeezenet: Alexnet-level accuracy with 50x fewer parameters and 0.5 mb model size
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Autoencoding beyond pixels using a learned similarity metric
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Deep multi-scale video prediction beyond mean square error
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A note on the evaluation of generative models
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Deep feature consistent variational autoencoder
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Automatic differentiation in pytorch
A. Paszke, S. Gross, S. Chintala, G. Chanan, E. Yang, Z. DeVito, Z. Lin, A. Desmaison, L. Antiga, and A. Lerer · 2017
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The unreasonable effectiveness of deep features as a perceptual metric
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Deepfakes: Trick or treat?
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