Style Loss is used to improve style transfer capabilities in GANs by ensuring that the generated image captures the style (textures, colors, patterns) of a target reference image while maintaining content from the input image. Here are the steps you can follow:
- Style Loss: Measures the difference between the Gram matrix of feature activations of the generated image and the reference image. The Gram matrix captures the style by considering correlations between different feature maps.
- Content Loss: Keeps the content of the input image intact by comparing the feature activations of the generated image and the input image.
- Total Loss: A combination of content loss and style loss is used to balance between preserving content and transferring style.
Here is the code snippet you can refer to:
In the above code, we are using the following key points:
- Style Loss: Computed as the difference in Gram matrices between the generated image and the style reference image.
- Content Loss: Ensures the generated image retains the content of the input image.
- VGG19 as Feature Extractor: Utilizes a pretrained VGG19 to extract deep features from images, which is crucial for capturing style and content.
- Training Objective: The generator is trained to minimize the combined loss, ensuring both style transfer and content preservation.
Hence, by referring to the above, you can apply style loss to improve style transfer capabilities in GANs for image manipulation
