Authors
Ali Borji
Publication date
2019/2/9
Journal
Computer Vision and Image Understanding
Description
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 …
Total citations
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Scholar articles
A Borji - Computer vision and image understanding, 2019
A Borji - arXiv preprint arXiv:1802.03446, 2018
A Borji - URL https://arxiv. org/abs, 1802
A Borji - arXiv preprint arXiv:1802.03446, 2018
A Borji - arXiv preprint arXiv:1802.03446, 2018
A Borji - arxiv, 1802
A Borji - URL https://arxiv. org/abs/2103.09396
A Borji - arXiv preprint arXiv:1802.03446, 2018
AB Pros - DOI: https://doi. org/10.1016/j. cviu, 2018
A Borji - arXiv preprint arXiv:1802.03446, 2018
A Borji - arXiv preprint arXiv:1802.03446, 2018
A Borji - 2018
A Borji - 1802
A Borji - URL http://arxiv. org/abs/1802.03446 v1