  Evolution of visual object recognition architectures based on Convolutional
Neural Networks & Convolutional Deep Belief Networks paradigms has
revolutionized artificial Vision Science. These architectures extract & learn
the real world hierarchical visual features utilizing supervised & unsupervised
learning approaches respectively. Both the approaches yet cannot scale up
realistically to provide recognition for a very large number of objects as high
as 10K. We propose a two level hierarchical deep learning architecture inspired
by divide & conquer principle that decomposes the large scale recognition
architecture into root & leaf level model architectures. Each of the root &
leaf level models is trained exclusively to provide superior results than
possible by any 1-level deep learning architecture prevalent today. The
proposed architecture classifies objects in two steps. In the first step the
root level model classifies the object in a high level category. In the second
step, the leaf level recognition model for the recognized high level category
is selected among all the leaf models. This leaf level model is presented with
the same input object image which classifies it in a specific category. Also we
propose a blend of leaf level models trained with either supervised or
unsupervised learning approaches. Unsupervised learning is suitable whenever
labelled data is scarce for the specific leaf level models. Currently the
training of leaf level models is in progress; where we have trained 25 out of
the total 47 leaf level models as of now. We have trained the leaf models with
the best case top-5 error rate of 3.2% on the validation data set for the
particular leaf models. Also we demonstrate that the validation error of the
leaf level models saturates towards the above mentioned accuracy as the number
of epochs are increased to more than sixty.
