How to Evaluate Generative Adversarial Networks

Generative adversarial networks, or GANs for short, are an effective deep learning approach for developing generative models.

Unlike other deep learning neural network models that are trained with a loss function until convergence, 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.

Instead, a suite of qualitative and quantitative techniques have been developed to assess the performance of a GAN model based on the quality and diversity of the generated synthetic images.

In this post, you will discover techniques for evaluating generative adversarial network models based on generated synthetic images.

After reading this post, you will know:

• There is no objective function used when training GAN generator models, meaning models must be evaluated using the quality of the generated synthetic images.
• Manual inspection of generated images is a good starting point when getting
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