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The Flaw Lurking In Every Deep Neural Net

The Flaw Lurking In Every Deep Neural Net: mikejuk (1801200) writes “A recent paper, ‘Intriguing properties of neural networks,’ by Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian Goodfellow and Rob Fergus, a team that includes authors from Google’s deep learning research project, outlines two pieces of news about the way neural networks behave that run counter to what we believed — and one of them is frankly astonishing. Every deep neural network has ‘blind spots’ in the sense that there are inputs that are very close to correctly classified examples that are misclassified. To quote the paper: ‘For all the networks we studied, for each sample, we always manage to generate very close, visually indistinguishable, adversarial examples that are misclassified by the original network.’ To be clear, the adversarial examples looked to a human like the original, but the network misclassified them. You can have two photos that look not only like a cat but the same cat, indeed the same photo, to a human, but the machine gets one right and the other wrong. What is even more shocking is that the adversarial examples seem to have some sort of universality. That is a large fraction were misclassified by different network architectures trained on the same data and by networks trained on a different data set. You might be thinking ‘so what if a cat photo that is clearly a photo a cat is recognized as a dog?’ If you change the situation just a little and ask what does it matter if a self-driving car that uses a deep neural network misclassifies a view of a pedestrian standing in front of the car as a clear road? There is also the philosophical question raised by these blind spots. If a deep neural network is biologically inspired we can ask the question, does the same result apply to biological networks? Put more bluntly, ‘Does the human brain have similar built-in errors?’ If it doesn’t, how is it so different from the neural networks that are trying to mimic it?” Share on Google+

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