Auxiliary Generative Adversarial Networks with Iliustration2Vec and Q-Learning based Hyperparameter Optimisation for Anime Image Synthesis

Harnessing the power of Generative Adversarial Networks (GANs) for the specialised task of anime face generation, this study introduces enhanced models of Auxiliary Classifier GAN (AC-GAN) and Wasserstein Auxiliary Classifier GAN (WAC-GAN) with modified network architectures and reinforcement learning-based hyperparameter optimisation. These models are uniquely adapted to handle the distinct nuances of anime-style imagery, a domain where conventional GANs often stumble due to complex stylistic variations and a heightened risk of mode collapse. Novel elements of our approach include, (1) modification of existing generator and discriminator architectures of both AC-GAN and WAC-GAN, (2) Q-learning based optimal hyperparameter selection, and (3) Illustration2Vec (I2V)-based automated attribute label extraction. Specifically, the Q-learning method is employed for hyperparameter search which effectively explores the search space of key network configurations by fulfilling the principles of Bellman optimality. Besides that, a deep learning-based 12V's method is utilised to generate attribute class labels and latent vectors to inform the generation process. Furthermore, we augment AC-GAN and WAC-GAN with additional layers to enhance their feature learning and generative capabilities. The insertion of these additional layers is calibrated based on the optimised network learning settings as well as the class labels derived from 12V, to fine-tune model scalability and diversity. Our experimental studies indicate that the conjunction of these techniques has led to a significant improvement in generating high-fidelity anime faces, adeptly handling the diverse and complex attributes inherent in anime-style imagery. The proposed strategies also showcase the potential of our customised AC-GAN and WAC-GAN models to master the nuanced art of anime face generation.