The evaluation of GANs with qualitative methods focuses on exploratory data analysis. In such methods, the researcher evaluates the fake samples by visual inspection. This can be done independently from other samples or with respect to real samples. Qualitative methods are useful as they can quickly provide information about issues with your current GAN experiment related to image quality, image variety, and the violation of specifications.
In GAN literature, the visual inspection of samples is a very common practice and authors use it to quickly confirm that they have not observed mode collapse or that their framework is robust to mode collapse if some criteria is met (Arjovsky et al., 2017; Gulrajani et al., 2017; Mao et al., 2016; and Radford et al., 2015).
Qualitative methods for evaluation are very useful to quickly detect problems with fake data. This...
