Understanding deep learning requires re-thinking generalization
> Take the same training data, but this time randomly jumble the labels (i.e., such that there is no longer any genuine correspondence between the label and what’s in the image).<p>What's a 'genuine correspondence'? The network has clearly picked out some image features that correspond to the assigned labels. Just because they're not the features you're thinking of doesn't mean they don't exist.