Can We Use Saliency Maps for the Evaluation of Generative Image Models?

The paper proposes a new evaluation framework for generative image models that uses a generator‑specific classifier trained to distinguish real images from those produced by a particular model. By analyzing the classifier’s decisions with saliency maps, the authors show that the maps reveal model‑specific artifacts rather than a universal notion of fakeness, and that combining classification accuracy with saliency information yields a composite score that aligns with human judgments. This approach avoids reliance on large pretrained backbones and works solely with real and generated samples from the evaluated model.

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