Editing Generative Adversarial Networks (section)

== <span style="color: #FFFFFF;">Evaluating</span> ==
Expert GAN evaluation is multi-faceted:

'''FID (Fréchet Inception Distance)''': Computes the Fréchet distance between the distribution of Inception v3 features for 50k real and 50k generated samples. Lower is better. FID captures both quality (sharpness, realism) and diversity (mode coverage). It is the standard metric but has known limitations: it is sensitive to the number of samples and the pre-trained feature extractor.

'''Precision and Recall for generative models''': Kynkäänniemi et al. (2019) proposed separate precision (sample quality) and recall (mode coverage) metrics. A GAN with high precision but low recall has mode collapse. This is more informative than FID alone.

'''IS (Inception Score)''': Measures both quality (samples should be classifiable) and diversity (class distribution should be uniform). Less reliable than FID because it doesn't compare to real data.

'''Perceptual user studies''': Human raters are shown real and generated images; measure discrimination accuracy (lower = more realistic). This remains the gold standard for applications where human perception is the target.

Expert practitioners also perform '''interpolation tests''': sample two latent vectors z1 and z2, interpolate between them, and verify that the generated images transition smoothly and meaningfully — indicating a well-structured latent space rather than memorization.
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