Training method for convolutional neural networks for use in artistic style transfers for video

Abstract

Systems and methods for use in training a convolutional neural network (CNN) for image and video transformations. The CNN is trained by adding noise to training data set images, transforming both the noisy image and the source image, and then determining the difference between the transformed noisy image and the transformed source image. The CNN is further trained by using an object classifier network and noting the node activation levels within that classifier network when transformed images (from the CNN) are classified. By iteratively adjusting the CNN to minimize a combined loss function that includes the differences between the node activation levels for the transformed references images and when transformed source are classified and the differences between the transformed noisy image and the transformed source image, the artistic style being transferred is maintained in the transformed images.