"In this work, we show that, contrary to expectations, a great deal of image statistics are captured by the structure of a convolutional image generator rather than by any learned capability. This is particularly true for the statistics required to solve various image restoration problems, where the image prior is required to integrate information lost in the degradation processes.
To show this, we apply untrained ConvNets to the solution of several such problems. Instead of following the common paradigm of training a ConvNet on a large dataset of example images, we fit a generator network to a single degraded image. In this scheme, the network weights serve as a parametrization of the restored image. The weights are randomly initialized and fitted to maximize their likelihood given a specific degraded image and a task-dependent observation model.
We show that this very simple formulation is very competitive for standard image processing problems such as denoising, inpainting and super-resolution. This is particularly remarkable because no aspect of the network is learned from data; instead, the weights of the network are always randomly initialized, so that the only prior information is in the structure of the network itself. To the best of our knowledge, this is the first study that directly investigates the prior captured by deep convolutional generative networks independently of learning the network parameters from images."
PS. This makes me wonder whether and to what degree the structure of the brain's connectome might be a necessary prior for AGI.