Hi everyone !

I am looking for papers that would show that deep learning can “invent” things, especially in the domain of super-resolution/upsampling. I only find papers that boasts the merits of their method, but not papers that show that their network can mistake high frequency features and noise or stuff like that…

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Anyone?

Thanks!