Bibek Chalise
BIbek Chalise


BIbek Chalise

Evaluation of GANs Model

Evaluation of GANs Model

Learn why evaluating GANs is hard?

Bibek Chalise's photo
Bibek Chalise
·Jan 3, 2023·

1 min read

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.

  1. Fidelity

  2. 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.

  1. Pixel Distance

  2. Feature Distance

Pixel Distance

Real - Fake = Abs. 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

Feature Distance

Fig: Feature Distance

With this technique, even a shift in pixel values won't make any difference.


-> Pixel distance is simple but unreliable.

->Feature distance uses the higher-level features of an image, making it more reliable.

Share this