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> the human mind is not ML Pooling layers in CNNs throw away lots of information about spatial co-occurrence of features, which leads to a possibility of adver
by red75prime 9y ago
> the human mind is not ML
Pooling layers in CNNs throw away lots of information about spatial co-occurrence of features, which leads to a possibility of adversarial images where adversarial features are scattered all over the place and so they don't significantly affect humans' visual processing.
The conclusion should be "Human mind is not that kind of ML".
- yters 9y agoCNNs are the best we have. If they can't deal with adversarial examples, then what can?
- red75prime 9y agoHinton'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.