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Detecting GAN generated errors
[article]
2019
arXiv
pre-print
Despite an impressive performance from the latest GAN for generating hyper-realistic images, GAN discriminators have difficulty evaluating the quality of an individual generated sample. This is because the task of evaluating the quality of a generated image differs from deciding if an image is real or fake. A generated image could be perfect except in a single area but still be detected as fake. Instead, we propose a novel approach for detecting where errors occur within a generated image. By
arXiv:1912.00527v1
fatcat:qyzf7ix3mbfklackr2xrezpogu