  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.
