Perceptual Losses for Real Time Style Transfer and Super Resolution
We consider image transformation problems, where an input image is transformed into an output image. Recent methods for such problems typically train feed forward convolutional neural networks using a per pixel loss between the output and ground truth images. Parallel work has shown that high quality images can be generated by defining and optimizing perceptual loss functions based on high level features extracted from pretrained networks. We combine the benefits of both approaches, and propose the use of perceptual loss functions for training feed forward networks for image transformation tasks. We show results on image style transfer, where a feed forward network is trained to solve the optimization problem proposed by Gatys et al. in real time. Compared to the optimization based method, our network gives similar qualitative results but is three orders of magnitude faster. We also experiment with single image super resolution, where replacing a per pixel loss with a perceptual loss gives visually pleasing results.
ECCV