| Could be wrong too, ML is not my field. Basically, a generator neural network has two things which affect its output: an input and some parameters (weights). Let's use the setting of the task for denoising images. They use a network who's output is to is the denoised image, and compare it to the noisy image to get a score for how good the denoised output was. Now the strange thing is, for the input of the network, they just use random garbage. The only thing they move around to try get a good denoising score are the parameters of the network (not the input). They find that by only adjusting the parameters, even with fixed random crap inputs, if they find the parameter setting which minimizes the noise, they actually still get great looking results. This suggests that the networks they tested this method with (other researchers work who do well on this task) are based much more in the inherent structure of the network, rather than the model refinement from training on thousands of images, since even given random crap, they generates good results, as long as the parameters are tuned well to the noisy image. |