Image-based model parameter optimization using Model-Assisted Generative Adversarial Networks
This addresses parameter tuning for image-based simulations, particularly in experimental contexts, but is incremental as it builds on existing GAN methods.
The paper tackles the problem of optimizing model parameters to match simulated images with true images by using a model-assisted GAN, achieving excellent agreement between generated and true parameters in case studies.
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. In the case of a real-world experiment, the true images are experimental data with unknown true model parameter values, and the fake images are produced by a simulation that takes the model parameters as input. The model-assisted GAN uses a convolutional neural network to emulate the simulation for all parameter values that, when trained, can be used as a conditional generator for fast fake-image production.