Dynamic Neural Style Transfer for Artistic Image Generation using VGG19
This work addresses inefficiencies in artistic image generation for AI applications, but it is incremental as it builds on existing CNN-based style transfer methods.
The paper tackled the problem of slow processing, limited style choices, and inflexible style weight adjustments in neural style transfer by proposing a system using VGG19 for feature extraction, resulting in reduced processing time and flexible stylization while maintaining content integrity.
Throughout history, humans have created remarkable works of art, but artificial intelligence has only recently started to make strides in generating visually compelling art. Breakthroughs in the past few years have focused on using convolutional neural networks (CNNs) to separate and manipulate the content and style of images, applying texture synthesis techniques. Nevertheless, a number of current techniques continue to encounter obstacles, including lengthy processing times, restricted choices of style images, and the inability to modify the weight ratio of styles. We proposed a neural style transfer system that can add various artistic styles to a desired image to address these constraints allowing flexible adjustments to style weight ratios and reducing processing time. The system uses the VGG19 model for feature extraction, ensuring high-quality, flexible stylization without compromising content integrity.