On the Evaluation of Conditional GANs

By Terrance Devries, Adriana Romero, Luis Villaseñor Pineda, Graham W. Taylor, Michal Drozdzal. 2019

51 citations recorded by the source corpus.

Conditional Generative Adversarial Networks (cGANs) are finding increasingly widespread use in many application domains. Despite outstanding progress, quantitative evaluation of such models often involves multiple distinct metrics to assess different desirable properties, such as image quality, conditional consistency, and intra-conditioning diversity. In this setting, model benchmarking becomes a challenge, as each metric may indicate a different "best" model. In this paper, we propose the Frechet Joint Distance (FJD), which is defined as the Frechet distance between joint distributions of images and conditioning, allowing it to implicitly capture the aforementioned properties in a single metric. We conduct proof-of-concept experiments on a controllable synthetic dataset, which consistently highlight the benefits of FJD when compared to currently established metrics. Moreover, we use the newly introduced metric to compare existing cGAN-based models for a variety of conditioning modalities (e.g. class labels, object masks, bounding boxes, images, and text captions). We show that FJD can be used as a promising single metric for cGAN benchmarking and model selection. Code can be found at this https URL.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Generative Models, Evaluation & Benchmarks

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

In Computer Vision and Pattern Recognition

In Generative Models

In Evaluation & Benchmarks

Citations in the corpus

Adversarial Text-to-Image Synthesis: A Review (2021, background): “In [170], a joint Fr´echet distance metric is proposed which aims at providing a single score to evaluate various conditional modalities by taking both image and conditioning information into account.”