Lecture 9 | CNN Architectures

left: setting; right: tricks on each gpu, there's only one half of all the neurons

training classification early -> more gradient in such a deep NN

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from test error -> deeper is worse -> overfitting ???
from training error -> deeper is worse -> not overfitting !!! -> optimization problem ??? -> deeper means harder to train ???

cifar -> smaller than imagenet

stack MLP on top of the conv

local network -> precursor of googlenet