Neural Style Transfer: A Critical Review

Abstract

Neural Style Transfer (NST) is a class of software algorithms that allows us to transform scenes, change/edit the environment of a media with the help of a Neural Network. NST finds use in image and video editing software allowing image stylization based on a general model, unlike traditional methods. This made NST a trending topic in the entertainment industry as professional editors/media producers create media faster and offer the general public recreational use. In this paper, the current progress in Neural Style Transfer with all related aspects such as still images and videos is presented critically. The authors looked at the different architectures used and compared their advantages and limitations. Multiple literature reviews focus on the Neural Style Transfer of images and cover Generative Adversarial Networks (GANs) that generate video. As per the authors' knowledge, this is the only research article that looks at image and video style transfer, particularly mobile devices with high potential usage. This article also reviewed the challenges faced in applying for video neural style transfer in realtime on mobile devices and presents research gaps with future research directions. NST, a fascinating deep learning application, has considerable research and application potential in the coming years.

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“…Generative Adversarial Networks (GANs) are typical networks that are often used to stigmatize data from the conceptual stage [25]. Therefore, GAN is a potential candidate for producing images or videos with a series of networks that control it [26].…”

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“…[71], computational speed-up [34], [11], etc. Another direction investigates the suitability of applying NST in other broader areas [33], [60].…”

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