I think the obvious answer to this is to use a generative adversarial approach, but the more I think about that the more difficult it sounds in practice. Say on

I think the obvious answer to this is to use a generative adversarial approach, but the more I think about that the more difficult it sounds in practice. Say once you've trained in the initial weights of your classifier from your training set, you have a generator start adding distortions, occlusion, and noise to the training set images, then train the classifier to recognize those as true positives. Of course the difficult part is defining a generator architecture that can learn to generate a wide enough variety of distortions, occlusions and noise...