Uses bijective networks to identify large subspaces of invariance-based vulnerability and introduces the independence cross-entropy loss which partially alleviates it.
Adversarial examples trained on an ensemble of CNNs with a retinal preprocessing layer reduce the accuracy of time-limited humans in a two alternative forced choice task.