Neural Style Transfer - B. Sai karthik; K. Satish Kumar; G. Aarush Raj; P. Sathwik; S. Dharanishwar Reddy (Journal of Data Mining and Management, 2025)
Neural Style Transfer (NST) is a deep learning method that creates a new image by combining the content of one image with the style of another. This technique relies on Convolutional Neural Networks (CNNs) to extract high-level content features from deeper layers and style features from lower layers of a pre-trained deep learning model. The goal of this project is to enhance the final image so that it closely resembles the original image in terms of content, while still capturing the style and texture from the reference image. The deep learning model is trained using two types of losses: content loss and style loss. The content loss ensures that the main structure and meaning of the original image are preserved, while the style loss maintains the color, texture, and patterns from the style image. The model consists of 19 layers, including 16 convolutional layers and 3 fully connected layers, making it both simple and effective for tasks like feature extraction and image classification.