Evaluation of GANs Model
Learn why evaluating GANs is hard?
Computer Engineer Sudent with deep Interest in data science.
For classification in supervised learning, there are labels which make it easy to classify. But since GANs are unsupervised, it is not easy to decode the image generated from a random noise vector as real or fake. There is no clear 1 or 0.
A discriminator cannot be used for evaluation, because it overfits the generator it's trained with.
Two important properties when it comes to generating images from GANs.
Fidelity
Diversity
Fidelity deals with:
-> How good are the fake images?
-> How far is the fake image from the real one?
Diversity deals with:
You want a GAN that can generate a variety of different images.
Comparing images
There are two ways to compare real and fake images generated by Generator.
Pixel Distance
Feature Distance
Pixel Distance
Fig: Real Image - Fake Image = Abs. distance
This is not a great measure, if a single pixel is shifted, the abs. the difference will be significantly changed.
Feature Distance
Fig: Feature Distance
With this technique, even a shift in pixel values won't make any difference.
Conclusion:
-> Pixel distance is simple but unreliable.
->Feature distance uses the higher-level features of an image, making it more reliable.
