  Deep convolutional neural networks have proven to be well suited for image
classification applications. However, if there is distortion in the image, the
classification accuracy can be significantly degraded, even with
state-of-the-art neural networks. The accuracy cannot be significantly improved
by simply training with distorted images. Instead, this paper proposes a
multiple neural network topology referred to as a selective deep convolutional
neural network. By modifying existing state-of-the-art neural networks in the
proposed manner, it is shown that a similar level of classification accuracy
can be achieved, but at a significantly lower cost. The cost reduction is
obtained primarily through the use of fewer weight parameters. Using fewer
weights reduces the number of multiply-accumulate operations and also reduces
the energy required for data accesses. Finally, it is shown that the
effectiveness of the proposed selective deep convolutional neural network can
be further improved by combining it with previously proposed network cost
reduction methods.
