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I'm having trouble understanding why the success of pooling would be deemed unfortunate. Max-pooling or average-pooling are essentially learning something about
by msaroufim 12y ago
I'm having trouble understanding why the success of pooling would be deemed unfortunate. Max-pooling or average-pooling are essentially learning something about a pair of contiguous features: while max-pooling saves only the most prominent/largest value, average pooling compresses information using the mean. Saying this is unfortunate from Hinton's standpoint amounts to saying that it is very unlikely to see any sort of pooling behavior at the level of neuronal populations. At a physiological and psychological level, what would pooling equate to?
- zackchase 12y agoI think the intuition behind max-pooling is that it says "something is in this local region", without overly specifically saying where it is. Intuitively, a human may detects an edge or intense bright light, but not care so much about precisely where it is in the field of vision. After many successive layers of max-pooling, however (if the pools are not over-lapping), even somewhat course information about locality is lost. I believe Hinton objects to this gross loss of spatial information for two reasons: 1) Humans don't lose so much spatial information, and Hinton would like his models to ultimately capture a neurologically plausible computation. 2) It may not be necessary for object detection (Imagenet), but it would likely be important for more sophisticated tasks.
- bainsfather 12y agoHe also 'did not like' Support Vector Machines, back when they were the best method for image recognition. His reason was that SVMs were a 'dead end' - they were not a step on the path to human-level image recognition. His argument now seems pretty valid. I think he is saying the same thing about Max Pooling. Just my guess.