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The neural network is likely handling it just fine: A classifier generally outputs a vector of weights, so it’s likely classifying, say [0.8, 0.75] and then th

The neural network is likely handling it just fine: A classifier generally outputs a vector of weights, so it’s likely classifying, say [0.8, 0.75] and then the output is selecting the highest and saying “bunny”. Then you rotate it, and the classifier says [0.75, 0.8] and the output says “duck”. This is completely reasonable on the part of the network: all things being equal, animals generally appear in certain orientations and we should prefer the interpretation of the amigbuity which respects this alignment, slightly. Example: “bill” down, it looks more like a duck because rabbits rarely have their head in that alignment, while “ears up” it looks more like a rabbit since ducks rarely hold their bills that way. The problem is actually in how we represent probabilistic information to humans, aka “why the weather man is always wrong”, so it seems like the classifier is randomly flapping when it’s actually perfectly correctly adjusting its distribution of answers based on information we give it.

mannykannot 8y ago
It works that way for me when I play it. To see this, it seems to help to look at the picture but have half an eye on the changing NN evaluation. I don't think my perception is simply responding to the changing NN output, as there are times when I disagree with it. My guess is that, by dividing my attention, I reevaluate the picture more frequently.
twanvl 8y ago
> A classifier generally outputs a vector of weights, so it’s likely classifying, say [0.8, 0.75] and then the output is selecting the highest and saying “bunny”. Then you rotate it, and the classifier says [0.75, 0.8] and the output says “duck”. The original post actually included the predicted probabilities, which are around 80% for duck or rabbit and 0% for the other class. So the neural network really is overconfident.

mannykannot 8y ago

It works that way for me when I play it. To see this, it seems to help to look at the picture but have half an eye on the changing NN evaluation. I don't think my perception is simply responding to the changing NN output, as there are times when I disagree with it. My guess is that, by dividing my attention, I reevaluate the picture more frequently.

twanvl 8y ago

> A classifier generally outputs a vector of weights, so it’s likely classifying, say [0.8, 0.75] and then the output is selecting the highest and saying “bunny”. Then you rotate it, and the classifier says [0.75, 0.8] and the output says “duck”. The original post actually included the predicted probabilities, which are around 80% for duck or rabbit and 0% for the other class. So the neural network really is overconfident.