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"how much the feature is responsible for the overall derivative" part is obvious. But I'm curious how the assumption can be justified - how do you isolate a fea
by jeromebaek 8y ago
"how much the feature is responsible for the overall derivative" part is obvious. But I'm curious how the assumption can be justified - how do you isolate a feature to a certain linear subset? And how do you know that this feature is (i.e.) "stripes"? The mapping from vectors to human language is the part that seems hard.
- goldenkey 8y agoBy training a binary linear classifier over the vector space of the other NNs responses to a set of inputs with that specific feature. Language shouldn't be necessary if your feature can be conveyed through examples. But yes, it's a big assumption to say that all features can be isolated as linearly decidable subsets of the activation space. I would guess one could get better results with stronger, non linear classifiers combined with more abstract generalizations to directional derivatives.