Can deep learning be a good way to learn a "High-quality" simple functions for images? For example, identical transformation, rotation, translation, even a linear mapping.
closed as unclear what you're asking by YiFan, NCh, Lee David Chung Lin, Lord Shark the Unknown, Leucippus Mar 25 at 5:22
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The question is interesting , but vague , so may answer inherits the last property. In deep learning the models are highly nonlinear functions with lots of parameters . One consequence is that this models are very flexible - in the limit they can approximate anything, including any kind of linear mappings . However ,even if the data has a linear dependency , what you probably get after training such a model is a complicated non-linear function (but the quality of the predictions can be good ).
If you know that the data comes from a linear model, it is probably best to try to learn such a model using a more specific algorithm ( a random example: On Learning Rotations - Raman Arora ).