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A relevant extension of\nthis model is conditional GANs (cGANs), where the introduction of external\ninformation allows to determine specific representations of the generated\nimages. In this work, we evaluate encoders to inverse the mapping of a cGAN,\ni.e., mapping a real image into a latent space and a conditional\nrepresentation. This allows, for example, to reconstruct and modify real images\nof faces conditioning on arbitrary attributes. Additionally, we evaluate the\ndesign of cGANs. 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