Image style transfer is an underdetermined problem, where a large number of solutions can satisfy the same constraint (the content and style). Although there have been some efforts to improve the diversity of style transfer by introducing an alternative diversity loss, they have restricted generalization, limited diversity and poor scalability. In this paper, we tackle these limitations and propose a simple yet effective method for diversified arbitrary style transfer. The key idea of our method is an operation called deep feature perturbation (DFP), which uses an orthogonal random noise matrix to perturb the deep image feature maps while keeping the original style information unchanged. Our DFP operation can be easily integrated into many existing WCT (whitening and coloring transform)-based methods, and empower them to generate diverse results for arbitrary styles. Experimental results demonstrate that this learning-free and universal method can greatly increase the diversity while maintaining the quality of stylization.

Although existing image inpainting approaches have been able to produce visually realistic and semantically correct results, they produce only one result for each masked input. In order to produce multiple and diverse reasonable solutions, we present Unsupervised Cross-space Translation Generative Adversarial Network (called UCTGAN) which mainly consists of three network modules: conditional encoder module, manifold projection module and generation module. The manifold projection module and the generation module are combined to learn one-to-one image mapping between two spaces in an unsupervised way by projecting instance image space and conditional completion image space into common low-dimensional manifold space, which can greatly improve the diversity of the repaired samples. For understanding of global information, we also introduce a new cross semantic attention layer that exploits the long-range dependencies between the known parts and the completed parts, which can improve realism and appearance consistency of repaired samples. Extensive experiments on various datasets such as CelebA-HQ, Places2, Paris Street View and ImageNet clearly demonstrate that our method not only generates diverse inpainting solutions from the same image to be repaired, but also has high image quality.

Recent studies using deep neural networks have shown remarkable success in style transfer, especially for artistic and photo‐realistic images. However, these methods cannot solve more sophisticated problems. The approaches using global statistics fail to capture small, intricate textures and maintain correct texture scales of the artworks, and the others based on local patches are defective on global effect. To address these issues, this study presents a unified model [global and local style network (GLStyleNet)] to achieve exquisite style transfer with higher quality. Specifically, a simple yet effective perceptual loss is proposed to consider the information of global semantic‐level structure, local patch‐level style, and global channel‐level effect at the same time. This could help transfer not just large‐scale, obvious style cues but also subtle, exquisite ones, and dramatically improve the quality of style transfer. Besides, the authors introduce a novel deep pyramid feature fusion module to provide a more flexible style expression and a more efficient transfer process. This could help retain both high‐frequency pixel information and low‐frequency construct information. They demonstrate the effectiveness and superiority of their approach on numerous style transfer tasks, especially the Chinese ancient painting style transfer. Experimental results indicate that their unified approach improves image style transfer quality over previous state‐of‐the‐art methods.