Evaluation of Generative Adversarial Network for Human Face Image Synthesis (original) (raw)

2020 International Conference on Software, Telecommunications and Computer Networks (SoftCOM)

Abstract

Meaningful and objective evaluation metric for fair model comparison is crucial for further scientific progress in the field of deep generative modeling. Despite the significant progress and impressive results obtained by Generative Adversarial Networks in recent years, the problem of their objective evaluation remains open. In this paper, we give an overview of qualitative and quantitative evaluation measures most frequently used to assess the quality of generated images and learned representations of an adversarial network together with the empirical comparison of their performance on the problem of human face image synthesis. It is shown that evaluation scores of the two most widely accepted quantitative metrics, Inception Score (IS) and Fréchet Inception Distance (FID), do not correlate. The IS is not an appropriate evaluation metric for a given problem, but FID shows good performance that correlates well with a visual inspection of generated samples. The qualitative evaluation can be used to complement results obtained with quantitative evaluation - to gain further insight into the learned data representation and detect possible overfitting.

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