Generative Adversarial Networks and Applications in Bioinformatics

Abstract

Generative Adversarial Networks (GAN) are currently considered a state-of-the-art method for image generation. Recently, Deep Convolutional Generative Adversarial Networks (DCGAN) yielded promising results in protein contact maps generation. The algorithm generated realistic protein structures, which were less erroneous than previously used generative methods. However, DCGAN is notorious for being hard to train due to the limitations of its loss function and complications in optimization. Wasserstein Generative Adversarial Networks (WGAN) was proposed, employing the Wasserstein loss function that stabilizes training and alleviates some of the DCGAN's training problems. In this thesis, a hyperparameter grid search for DCGAN and WGAN was conducted on the CIFAR-10 dataset. Runs with different hyperparameters were compared using Fréchet Inception Distance to determine whether WGAN is more stable than DCGAN.