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Given an input image from a source domain and a guidance image from a targetdomain, unsupervised many-to-many image-to-image (UMMI2I) translation methodsseek to generate a plausible example from the target domain that preservesdomain-invariant information of the input source image and inherits thedomain-specific information from the guidance image. For example, whentranslating female faces to male faces, the generated male face should have thesame expression, pose and hair color as the input female image, and the samefacial hairstyle and other male-specific attributes as the guidance male image.Current state-of-the art UMMI2I methods generate visually pleasing images, but,since for most pairs of real datasets we do not know which attributes aredomain-specific and which are domain-invariant, the semantic correctness ofexisting approaches has not been quantitatively evaluated yet. In this paper,we propose a set of benchmarks and metrics for the evaluation of semanticcorrectness of these methods. We provide an extensive study of existingstate-of-the-art UMMI2I translation methods, showing that all methods, todifferent degrees, fail to infer which attributes are domain-specific and whichare domain-invariant from data, and mostly rely on inductive biases hard-codedinto their architectures.

Evaluation of Correctness in Unsupervised Many-to-Many Image Translation