  Recently, style transfer is a research area that attracts a lot of attention,
which transfers the style of an image onto a content target. Extensive research
on style transfer has aimed at speeding up processing or generating
high-quality stylized images. Most approaches only produce an output from a
content and style image pair, while a few others use complex architectures and
can only produce a certain number of outputs. In this paper, we propose a
simple method for representing style features in many ways called Deep Feature
Rotation (DFR), while not only producing diverse outputs but also still
achieving effective stylization compared to more complex methods. Our approach
is representative of the many ways of augmentation for intermediate feature
embedding without consuming too much computational expense. We also analyze our
method by visualizing output in different rotation weights. Our code is
available at https://github.com/sonnguyen129/deep-feature-rotation.
