"Perhaps the most significant implication of our result for deep learning is that it casts
doubt on the common view that it works by automatically discovering new representations of
the data, in contrast with other machine learning methods, which rely on predefined features
(Bengio et al., 2013). As it turns out, deep learning also relies on such features, namely the
gradients of a predefined function, and uses them for prediction via dot products in feature
space, like other kernel machines."
Huh. So the implication here is that a deep network can never generalize results to inputs classes that were not explicit in the training set? And would this result apply for networks trained with something other than gradient based methods?
Comments
"Perhaps the most significant implication of our result for deep learning is that it casts doubt on the common view that it works by automatically discovering new representations of the data, in contrast with other machine learning methods, which rely on predefined features (Bengio et al., 2013). As it turns out, deep learning also relies on such features, namely the gradients of a predefined function, and uses them for prediction via dot products in feature space, like other kernel machines."
Huh. So the implication here is that a deep network can never generalize results to inputs classes that were not explicit in the training set? And would this result apply for networks trained with something other than gradient based methods?