Condition monitoring of unseen machines or operating conditions has received increasing attention in recent years. This study presents a computationally efficient approach to domain adaptation inspired by individual standardization for different subjects employed in EEG emotion recognition. On unseen working conditions, the individual standardization approach increases model performance by 15% for ROC and by 34% for accuracy, compared to models without domain adaptation. The approach achieves competitive scores without changing any other model parameters.

Our goal with this survey is to provide an overview of the state of the art deep learning methods for face generation and editing using StyleGAN. The survey covers the evolution of StyleGAN, from PGGAN to StyleGAN3, and explores relevant topics such as suitable metrics for training, different latent representations, GAN inversion to latent spaces of StyleGAN, face image editing, cross-domain face stylization, face restoration, and even Deepfake applications. We aim to provide an entry point into the field for readers that have basic knowledge about the field of deep learning and are looking for an accessible introduction and overview.