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That's an advantage of convolutional nets. Deep fully connected nets don't do that afaik.

• IanCal 9y ago
  They do, at least as far as I understand the statement. Historically the big benefit was training them layer by layer, which was like training a feature detector then a feature of features detector etc. If that's still how they're trained (been nearly a decade for me now) then they discover features rather than you engineering them. This meant that you could train on large unlabelled data and then small amounts of labelled data.
  • jclos 9y ago
    Yeah now that I think about it my statement didn't make any sense, since each intermediate layer computes a projection of the previous one, which is technically feature learning. I still disagree with the original comment though, because the intermediate representations of the data computed by a fully connected network are nothing like the ones that would be built by a human doing feature engineering. The ones learned by a convolutional layer would be closer to human-understandable features.
• amelius 9y ago
  Loosely speaking, convolutional nets are just a smart way of computing a function that would otherwise take the computational load of a fully connected net.
• PeterisP 9y ago
  All kinds of other structures also do that - for example, the "family" of recurrent networks in many non-visual problems.