Progressive Semantic Image Synthesis via Generative Adversarial Network Semantic image synthesis via text description is a desirable and challenging task, which requires more protection of the text irrelevant content in the original image. Existing methods directly modify the original image, which become more difficult when encountering high resolution image, and the generated images are also blurred and lack in detail. This paper presents a novel network architecture to progressively manipulate an image starting from lowresolution, while introducing the original image of corresponding size at different stages with our proposed union module to avoid losing of detail. And the progressive design of the network allows us to modify the image from coarse into fine. Compared with the previous methods, our new method can successfully manipulate a high resolution image and generate a new image with background protection and fine details. The experimental results on CUB2002011 dataset show that the proposed approach outperforms existing methods in terms of image detail, background protection and high resolution generation.

On Conditioning GANs to Hierarchical Ontologies The recent success of Generative Adversarial Networks (GAN) is a result of their ability to generate high quality images from a latent vector space. An important application is the generation of images from a text description, where the text description is encoded and further used in the conditioning of the generated image. Thus the generative network has to additionally learn a mapping from the text latent vector space to a highly complex and multimodal image data distribution, which makes the training of such models challenging. To handle the complexities of fashion image and meta data, we propose Ontology Generative Adversarial Networks (OGANs) for fashion image synthesis that is conditioned on an hierarchical fashion ontology in order to improve the image generation fidelity. We show that the incorporation of the ontology leads to better image quality as measured by Frechet Inception Distance and Inception Score. Additionally, we show that the OGAN achieves better conditioning results evaluated by implicit similarity between the text and the generated image.