The key claim, from the original paper: > Second, we find that deep neural networks learn input-output mappings that are fairly discontinuous [...] Specificall

The key claim, from the original paper: > Second, we find that deep neural networks learn input-output mappings that are fairly discontinuous [...] Specifically, we find that we can cause the network to misclassify an image by applying a certain imperceptible perturbation [...] the same perturbation can cause a different network that was trained on a different subset of the dataset, to misclassify the same input. It's an interesting outcome -- but there are many deep-learning approaches and many different benchmarks, so it will be important to see if this is a misleading anecdote or indicative of a systematic problem. [1] http://cs.nyu.edu/~zaremba/docs/understanding.pdf http://cs.nyu.edu/~zaremba/docs/understanding.pdf