Hypernetwork functional image representation

We use a hypernetwork to automatically generate continuous functional representation of images at test time without any additional training. More precisely, the hypernetwork takes an image and returns weights to a target network representing the image. Since obtained representation is continuous, we can easily inspect the image at various resolutions. Finally, because we use a single hypernetwork responsible for creating individual image models, similar images have similar weights of their target networks. As a consequence, interpolation in the space of weights of target networks representing images shows properties similar to that of generative models. To experimentally evaluate the proposed mechanism, we apply it to image super-resolution. Despite of using a single model for various scale factors, we obtained the results comparable to existing super-resolution methods.