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| + | * **Theory of evolution to improve GAN training**: Generative adversarial networks (GANs) have achieved some spectacular results in the six years since they have been introduced. However, their training process is still fraught with issues such as mode collapse, non-convegence or gradients collapse. Those issues have still not been completely resolved, making multiple restarts and selection of well-performing Generator-Discriminator pairs part of GAN training process. The process of adversarial generator-discriminator training is not dissimilar from co-evolution of two adversarial species - such as for instance hosts and pathogens, except that rounds of mutation/recombination/selection in search of fitness optimum are replaced by gradient descent. Our goal is to investigate - both experimentally and theoretically - if we can further stabilize and improve GAN training with evolutionary mechanisms, such as speciation, aneuploidization, neutral variability buffering or meta-evolutionary mechanisms. This work wold have implications on developing efficient solutions to detecting GAN products (aka deepfakes). You will need to have experience with scientific computing in Python, ideally with PyTorch experience, and ideally you should have some knowledge of population genetics. Contact [[https://people.epfl.ch/andrei.kucharavy|Andrei Kucharavy]] for more information. | ||