Deep Learning Style Transfer – Tutorial

Style transfer neural networks offer a way to apply artistic styles to images by leveraging existing image classification networks as loss functions, which helps train a new network to blend the semantic features of a target image with the textures of a style image. The process involves two steps: training the style transfer network and then applying it to new images. Using JC Johnson’s fast-neural-style implementation and pre-trained VGG16 network, the training is efficiently conducted on Rescale's cloud platform with K80 GPUs. The tutorial provided details on executing a job on Rescale, which involves uploading necessary files such as the fast-neural-style software, a dataset, the target image, and the style image, and then running specific scripts to build and apply the style transfer model. Once trained, the model can be reused to style additional images without re-running the training process, and the results are accessible through Rescale's platform.