Measuring the tendency of CNNs to learn surface statistical regularities
Measuring the tendency of CNNs to learn surface statistical regularities Jo et al., arXiv’17
With thanks to Cris Conde for bringing this paper to my attention.
We’ve looked at quite a few adversarial attacks on deep learning systems in previous editions of The Morning Paper. I find them fascinating for what they reveal about the current limits of our understanding.
…humans are able to correctly classify the adversarial image with relative ease, whereas the CNNs predict the wrong label, usually with very high confidence. The sensitivity of high performance CNNs to adversarial examples casts serious doubt that these networks are actually learning high level abstract concepts. This begs the following question: How can a network that is not learning high level abstract concepts manage to generalize so well?
In this paper, Jo and Bengio conduct a series of careful experiments to try and discover what’s going on. The initial hypothesis runs like this:
- There are really only two ways we could be seeing the strong generalisation performance that we do. Either (a) the networks are learning high level concepts, or (b) there may be a number of superficial cues in images that are shared across training and test datasets, Continue reading