How On Earth Can We Evaluate the Generated Images By GANs?

Overview of Popular GANs Evaluation Metrics

GANs consist of two main networks generator and discriminator networks. A GAN generator model is trained using a second model called a discriminator that learns to classify images as real or generated. Both the generator and discriminator model are trained together to maintain an equilibrium. 

As such, there is no objective loss function used to train the GAN generator models and no way to objectively assess the progress of the training and the relative or absolute quality of the model from loss alone. This means that models must be evaluated using the quality of the generated synthetic images and manually inspecting the generated images.

This article is the third in a five articles series. In the coming one we will explore the popular GANs models and their main applications and we will conclude this series with an end-to-end GAN project.

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