Intriguing properties of neural networks
Authors: Joan Bruna, Christian Szegedy, Ilya Sutskever, Ian Goodfellow, Wojciech Zaremba, Rob Fergus, Dumitru Erhan
ICLR 2014

Abstract: Deep neural networks are highly expressive models that have recently achieved state of the art performance on speech and visual recognition tasks. While their expressiveness is the reason they succeed, it also causes them to learn uninterpretable solutions that could have counter-intuitive properties. In this paper we report two such properties. First, we find that there is no distinction between individual high level units and random linear combinations of high level units, according to various methods of unit analysis. It suggests that it is the space, rather than the individual units, that contains of the semantic information in the high layers of neural networks. Second, we find that deep neural networks learn input-output mappings that are fairly discontinuous to a significant extend. Specifically, we find that we can cause the network to misclassify an image by applying a certain imperceptible perturbation, which is found by maximizing the network's prediction error. In addition, the specific nature of these perturbations is not a random artifact of learning: the same perturbation can cause a different network, that was trained on a different subset of the dataset, to misclassify the same input.


Peer review:
[Review]:
Very interesting paper with very 'intriguing' results indeed.
I believe there are serious implications to these findings.
I wonder if this paper is in anyway related to this (https://moalquraishi.wordpress.com/2014/05/25/what-does-a-neural-network-actually-do/) post, where the main finding/claim is that depth in neural networks can be represented by additional nodes in the (single) hidden layer - which says that deep networks do not have a representational power over traditional NNs.