Since standard GANs lack a one-to-one correspondence between input and output, “SSIM” is not an appropriate metric for evaluation.
Instead, we can use “Frechet Inception Distance (FID)” as a metric. The inception score estimates the quality of a collection of synthetic images based on how well the top-performing image classification model “inceptionv3” classifies them as one of 1,000 known objects. For calculating FID, you can use pre-trained “inceptionv3” model from the “Deep Learning Toolbox” as follows.
net = imagePretrainedNetwork("inceptionv3”)
original_features = activations(net, original_images, 'avg_pool');
generated_features = activations(net, generated_images, 'avg_pool');
fid_score = wassersteinDistance(original_features, generated_features);
You can read about the model in the following links.
The implementation of the “Wasserstein-distance” can be found below.
In addition to this, we can also use "No Reference Image Quality" metrics to assess the performance of GAN model like “BRISQUE”, “NIQE” or “PIQE”.
Please refer to the following MathWorks documentation for more information on the "No Reference Image Quality" metrics:
Hope you find this information helpful.
