> ... so badly fooled while obscuring such a small amount of the face ... may go completely off the rails when presented with data not close enough to their tr

> ... so badly fooled while obscuring such a small amount of the face ... may go completely off the rails when presented with data not close enough to their training sets. Older research has looked at this, in the context of neural networks in self-driving cars. The nets take the easy way out: use just the most significant features (like road markers) to solve the problem. However, training sets may not contain roads where snow or fog is obscuring these significant features, which makes the nets fail, because they haven't learned to rely on other features too. In contrast, human vision is remarkably robust. http://repository.cmu.edu/cgi/viewcontent.cgi?article=1176&context=compsci http://repository.cmu.edu/cgi/viewcontent.cgi?article=1176&c... [pdf] "FeatureBoost: A Meta Learning Algorithm that Improves Model Robustness"