Hypernetworks mechanism allows to generate and train neural networks (target networks) with use of other neural network (hypernetwork). In this paper, we extend this idea and show that hypernetworks are able to generate target networks, which can be customized to serve different purposes. In particular, we apply this mechanism to create a continuous functional representation of images. Namely, the hypernetwork takes an image and at test time produces weights to a target network, which approximates its RGB pixel intensities. Due to the continuity of representation, we may look at the image at different scales or fill missing regions. Second, we demonstrate how to design a hypernetwork, which produces a generative model for a new data set at test time. Experimental results demonstrate that the proposed mechanism can be successfully used in super-resolution and 2D object modeling.
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