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

TL;DR

This paper introduces benchmarks and metrics to evaluate the semantic correctness of unsupervised many-to-many image translation methods, revealing their limitations in understanding domain-specific versus invariant attributes.

Contribution

It provides the first quantitative evaluation framework for semantic correctness in UMMI2I translation methods and analyzes their reliance on architectural biases.

Findings

All evaluated methods struggle to correctly identify domain-specific attributes.

Methods largely depend on architectural biases rather than data-driven attribute inference.

Current approaches do not reliably preserve semantic attributes across domains.

Abstract

Given an input image from a source domain and a guidance image from a target domain, unsupervised many-to-many image-to-image (UMMI2I) translation methods seek to generate a plausible example from the target domain that preserves domain-invariant information of the input source image and inherits the domain-specific information from the guidance image. For example, when translating female faces to male faces, the generated male face should have the same expression, pose and hair color as the input female image, and the same facial 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 are domain-specific and which are domain-invariant, the semantic correctness of existing approaches has not been quantitatively evaluated yet. In this paper, we propose a set of benchmarks and metrics for the evaluation of semantic correctness of these methods. We provide an extensive study of existing state-of-the-art UMMI2I translation methods, showing that all methods, to different degrees, fail to infer which attributes are domain-specific and which are domain-invariant from data, and mostly rely on inductive biases hard-coded into their architectures.