an M NIST that when we train against weak adversarial examples Emnes classifiers get more accurate so far that hasn't really held up on other data sets and hasn't held up when we train against stronger adversaries it seems like when you confront a really strong adversary you tend to have to give something up interesting this is such a compelling idea because it feels it feels like that's how us humans learn yeah the difficult cases we we try to think of what would we screw up and then we make sure we fix that yeah