Abstract
Recent image completion researches using deep neural networks approaches have shown remarkable progress by using generative adversarial networks (GANs). However, these approaches still suffer from the problems of large model sizes and lack of generality for various types of corruptions. In addition, the conditional GANs often suffer from the mode collapse and unstable training problems. In this thesis, we overcome these shortcomings in the previous models by proposing a lightweight model of conditional GANs and integrating a stable adversarial training strategy. Moreover, we present a new training strategy to train the model to learn how to complete different types of corruptions or missing regions in images. Experimental results demonstrate qualitatively and quantitatively that the proposed model provides significant improvement over state-of-the-art image completion methods on public datasets. In addition, we show that our model requires much less model parameters to achieve superior results for different types of unseen corruption masks.