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Computer Science > Computer Vision and Pattern Recognition

arXiv:1802.03446 (cs)
[Submitted on 9 Feb 2018 (v1), last revised 24 Oct 2018 (this version, v5)]

Title:Pros and Cons of GAN Evaluation Measures

Authors:Ali Borji
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Abstract:Generative models, in particular generative adversarial networks (GANs), have received significant attention recently. 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.
Subjects: Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:1802.03446 [cs.CV]
  (or arXiv:1802.03446v5 [cs.CV] for this version)
  https://doi.org/10.48550/arXiv.1802.03446
arXiv-issued DOI via DataCite

Submission history

From: Ali Borji [view email]
[v1] Fri, 9 Feb 2018 21:05:32 UTC (5,509 KB)
[v2] Mon, 21 May 2018 23:16:12 UTC (2,037 KB)
[v3] Thu, 7 Jun 2018 19:53:53 UTC (2,291 KB)
[v4] Thu, 6 Sep 2018 19:14:26 UTC (7,366 KB)
[v5] Wed, 24 Oct 2018 02:20:59 UTC (7,373 KB)
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