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Someone please explain this to me: I get that CNN's are unfit for learning different poses, rotations and such. However, the I don't get the face example at all
by GolDDranks 9y ago
Someone please explain this to me: I get that CNN's are unfit for learning different poses, rotations and such. However, the I don't get the face example at all.
Let's say there is a layer of neurons that, after some convolution and pooling, get some features like "noseness", "eyeness" and "mouthness". Unless the pool size in the pooling layer was big enough to include the whole face in the same pool, the parts are still spatially separate (although lower in resolution than in the original image).
When there is the next convolution layer, isn't it going to learn that "kernels that have eyeness up, noseness in the middle and mouthness bottom are the most facelike", similarly how the earlier layers learned to identify parts?
Am I missing something, or is it just a bad example?
- red75prime 9y agoTraining examples are unlikely to contain non-faces with high-noseness to the sides and high-eyeness in the center, but plenty of pictures with nose to the left, eye in the center, nose to the right, eye in the center. So there are many examples which increase the weights for faceness detector for pictures with noses on the sides, eye in the center, but there are only a small number of examples which decrease the weights for the said pictures.
- atupis 9y agoAnd that's why when you eg training CNN for MNIST you usually nudge and rotate numbers so you can get those weird edge cases.
- red75prime 9y agoI can't imagine a transformation which consistently produces nose-like features to the sides of an eye-like feature () for non-face source image, thus I doubt that the network will be able learn that such an image () is not a face. In the best case it will recognize the image as a tureen.