Neural Artistic Style Transfer with Conditional Adversaria
This addresses a bottleneck in artistic style transfer for users needing flexible, multi-style applications, though it appears incremental as it builds on existing GAN and cyclic consistency methods.
The paper tackles the problem of neural style transfer models being limited to a single style, requiring full retraining for new styles, by introducing a unidirectional-GAN model that achieves style-independent generation for any content and style image pair, resulting in a smaller model size and more efficient training.
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.