  Deep convolutional neural networks have shown high efficiency in computer
visions and other applications. However, with the increase in the depth of the
networks, the computational complexity is growing exponentially. In this paper,
we propose a novel solution to reduce the computational complexity of
convolutional neural network models used for many class image classification.
Our proposed technique breaks the classification task into two steps: 1)
coarse-grain classification, in which the input samples are classified among a
set of hyper-classes, 2) fine-grain classification, in which the final labels
are predicted among those hyper-classes detected at the first step. We
illustrate that our proposed classifier can reach the level of accuracy
reported by the best in class classification models with less computational
complexity (Flop Count) by only activating parts of the model that are needed
for the image classification.
