5 ms·
Hinton's "capsule networks", probably. Or any other future architecture which preserves positional information better.
by red75prime 9y ago
Hinton's "capsule networks", probably. Or any other future architecture which preserves positional information better.
- yters 9y agoIt is a problem inherent in any ML method with large VC dimension. The model will overfit, and when it overfits, adversarial examples become possible. The very nature of ML seems incapable of dealing with this dilemma. You either work within a very constrained domain (low VC) to ensure everything is covered, and thus are unable to fit complex datasets, or expand the domain (large VC) and thus overfit the datasets. It's the bias-variance tradeoff, and is insurmountable.
- red75prime 9y agoAdversarial examples are no longer adversarial in any meaningful sense, when humans are fooled by them too. We don't need to surmount BV tradeoff, we need to make sure that ML models make the same tradeoffs as humans.
- yters 9y agoEvidence that humans are fooled by adversarial examples? Optical illusions and other perception issues don't count, as explained by many others in this set of threads.
- red75prime 9y agoCamouflage. Paintings perceived as real 3d objects for a fraction of second.
- yters 9y agoYou think these are equivalent to changing a single pixel and causing a complete misperception of the object which is believed with almost 100% certainty?
- red75prime 9y agoSingle pixel attack is an artifact of the network architecture. It is not inherent vulnerability of every ML technique.