I Workshop on Applied Generative Models (AGM2021)

In the last years, the tremendous success of Generative Models (GM) turned this approach into a revolutionary tool applied in many fields, with a remarkable impact in disciplines such as material science, industrial design, super-resolution for medical imaging, computer graphics or simulation using virtual worlds.

These algorithms, rely on the use of machine learning methods to model the transformation from a pseudo-random distribution called the latent space to the distribution formed by the data. After the training, new data points with different variations can be generated by sampling the latent space.

Through the use of GM, many applications have been developed in the recent years: augmented creativity tools for design applications, generation of synthetic data in image detection problems, generation of layouts in architecture design, modeling for simulation purposes, advanced denoising procedures for improved data visualization, etc.

AGM2021 aims to bring together researchers from different domains to show the progress and impact of applying GM in their fields. AGM2021 aims to favour building up collaborative network of researchers to grow even more the spectrum of application domains for GM.

Chairs

• Carlos González Val, AIMEN Technological Center, Email: carlos.gonzalez@aimen.es
• Juan Manuel Fernández Montenegro, AIMEN Technological Center, Email: juan.fernandez@aimen.es
• Andrea Fenández Martinez, AIMEN Technological Center, Email: andrea.fernandez@aimen.es
• Santiago Muiños Landin, AIMEN Technological Center, Email: santiago.muinos@aimen.es
• Omar A. Mures, University of A Coruña (UDC), Email: omar.alvarez@udc.es
• Jose A. Iglesias-Guitian, University of A Coruña (UDC), CITIC - Centre for ICT Research, Email: j.iglesias.guitian@udc.es

Topics

The workshop will address the application of Generative Models with main focus in the following topics:

• Artificial Creativity
• Synthetic data generation for control and monitoring
• Content Generation
• Virtual environments
• Materials/chemical compound design using generative models

Other topics might be considered.