Hands-On Generative Adversarial Networks with Keras

Image quality

One important aspect of GAN evaluation is the image quality of the samples produced by the generator relative to the image quality of real samples. A common concern in training GANs is that the images generated by the generator can be blurry. Another concern is that the images can have checkerboard artifacts.

There are both quantitative and qualitative measures for assessing image quality. Whereas traditional quantitative metrics for image quality focus on measures such as distortion and signal to noise ratio, current metrics used in GANs focus on using embeddings obtained from neural networks that have been trained on image classification tasks.

Qualitative measures based on the visual inspection of fake samples can be a quick ...