  A neural artistic style transformation (NST) model can modify the appearance
of a simple image by adding the style of a famous image. Even though the
transformed images do not look precisely like artworks by the same artist of
the respective style images, the generated images are appealing. Generally, a
trained NST model specialises in a style, and a single image represents that
style. However, generating an image under a new style is a tedious process,
which includes full model training. In this paper, we present two methods that
step toward the style image independent neural style transfer model. In other
words, the trained model could generate semantically accurate generated image
under any content, style image input pair. Our novel contribution is a
unidirectional-GAN model that ensures the Cyclic consistency by the model
architecture.Furthermore, this leads to much smaller model size and an
efficient training and validation phase.

Computer Vision and Pattern Recognition;Artificial Intelligence;Information Retrieval;Machine Learning