2019/09/25 by Chunpeng Wu, Wu, Chunpeng, Wei Wen +5
Computer Science · #Computer Vision and Pattern Recognition (cs.CV) #Digital Media Forensic Detection #FOS: Computer and information sciences #FOS: Electrical engineering #Generative Adversarial Networks and Image Synthesis #Image Processing and 3D Reconstruction #Image and Video Processing (eess.IV) #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.1909.11308
openalex publication_date 2019/09/25 · openalex created_date 2019/10/03 · openalex updated_date 2026/07/28
Generative adversarial network (GAN) has greatly improved the quality of unsupervised image generation. Previous GAN-based methods often require a large amount of high-quality training data while producing a small number (e.g., tens) of classes. This work aims to scale up GANs to thousands of classes meanwhile reducing the use of high-quality data in training. We propose an image generation method based on conditional transferring features, which can capture pixel-level semantic changes when transforming low-quality images into high-quality ones. Moreover, self-supervision learning is integrated into our GAN architecture to provide more label-free semantic supervisory information observed from the training data. As such, training our GAN architecture requires much fewer high-quality images with a small number of additional low-quality images. The experiments on CIFAR-10 and STL-10 show that even removing 30% high-quality images from the training set, our method can still outperform previous ones. The scalability on object classes has been experimentally validated: our method with 30% fewer high-quality images obtains the best quality in generating 1,000 ImageNet classes, as well as generating all 3,755 classes of CASIA-HWDB1.0 Chinese handwriting characters.