Abstract:
n a broad range of computer vision tasks, convolutional neu-
ral networks (CNNs) are one of the most prominent tech-
niques due to their outstanding performance. Yet it is not
trivial to find the best performing network structure for a spe-
cific application because it is often unclear how the network
structure relates to the network accuracy. We propose an evo-
lutionary algorithm-based framework to automatically opti-
mize the CNN structure by means of hyper-parameters. Fur-
ther, we extend our framework towards a joint optimization
of a committee of CNNs to leverage specialization and coop-
eration among the individual networks. Experimental results
show a significant improvement over the state-of-the-art on
the well-established MNIST dataset for hand-written digits
recognition.
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