Quantitatively Evaluating GANs With Divergences Proposed for TrainingDownload PDF

15 Feb 2018 (modified: 07 Apr 2024)ICLR 2018 Conference Blind SubmissionReaders: Everyone
Abstract: Generative adversarial networks (GANs) have been extremely effective in approximating complex distributions of high-dimensional, input data samples, and substantial progress has been made in understanding and improving GAN performance in terms of both theory and application. However, we currently lack quantitative methods for model assessment. Because of this, while many GAN variants being proposed, we have relatively little understanding of their relative abilities. In this paper, we evaluate the performance of various types of GANs using divergence and distance functions typically used only for training. We observe consistency across the various proposed metrics and, interestingly, the test-time metrics do not favour networks that use the same training-time criterion. We also compare the proposed metrics to human perceptual scores.
TL;DR: An empirical evaluation on generative adversarial networks
Keywords: Generative adversarial networks
Data: [MNIST](https://paperswithcode.com/dataset/mnist)
Community Implementations: [![CatalyzeX](/images/catalyzex_icon.svg) 2 code implementations](https://www.catalyzex.com/paper/arxiv:1803.01045/code)
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