  In recent years, deep neural networks achieved state-of-the-art performance
on many computer vision tasks. The two most commonly observed drawbacks of
these deep neural networks are: the requirement of a massive amount of labeled
data and a vast number of parameters. In this work, we propose a theory named
Ikshana, to explain the functioning of the human brain, while humans understand
a natural scene/image. We have designed an architecture named IkshanaNet and
evaluated on the Cityscapes pixel-level semantic segmentation benchmark, to
show how to implement our theory in practice. The results showed that the
Ikshana theory could perform with less training data. Also, through some
experiments evaluated on the validation set, we showed that the Ikshana theory
can significantly reduce the number of parameters of the network. In
conclusion, a deep neural network designed by following the Ikshana theory will
learn better vector representations of the image, useful for any computer
vision task.
