Pros and cons of GAN evaluation measures

Generative models, in particular generative adversarial networks (GANs), have gained significant attention in recent years. A number of GAN variants have been proposed and have been utilized in many applications. Despite large strides in terms of theoretical progress, evaluating and comparing GANs r...

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Vydáno v:Computer vision and image understanding Ročník 179; s. 41 - 65
Hlavní autor: Borji, Ali
Médium: Journal Article
Jazyk:angličtina
Vydáno: Elsevier Inc 01.02.2019
Témata:
Deep learning
Evaluation
Generative adversarial nets
Generative models
Neural networks
Deep learning
Evaluation
Generative models
Neural networks
Generative adversarial nets
ISSN:1077-3142, 1090-235X
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Shrnutí:Generative models, in particular generative adversarial networks (GANs), have gained significant attention in recent years. A number of GAN variants have been proposed and have been utilized in many applications. Despite large strides in terms of theoretical progress, evaluating and comparing GANs remains a daunting task. While several measures have been introduced, as of yet, there is no consensus as to which measure best captures strengths and limitations of models and should be used for fair model comparison. As in other areas of computer vision and machine learning, it is critical to settle on one or few good measures to steer the progress in this field. In this paper, I review and critically discuss more than 24 quantitative and 5 qualitative measures for evaluating generative models with a particular emphasis on GAN-derived models. I also provide a set of 7 desiderata followed by an evaluation of whether a given measure or a family of measures is compatible with them.
ISSN:1077-3142
1090-235X
DOI:10.1016/j.cviu.2018.10.009