Image-based model parameter optimization using Model-Assisted Generative Adversarial Networks

Understand

We propose and demonstrate the use of a model-assisted generative adversarial network (GAN) to produce fake images that accurately match true images through the variation of the parameters of the model that describes the features of the images.

• The generator learns the model parameter values that produce fake images that best match the true images.
• Two case studies show excellent agreement between the generated best match parameters and the true parameters.
• The best match model parameter values can be used to retune the default simulation to minimize any bias when applying image recognition techniques to fake and true images.