  Identification of an entity that is of interest is prominent in any
intelligent system. The visual intelligence of the model is enhanced when the
capability of recognition is added. Several methods such as transfer learning
and zero shot learning help to reuse the existing models or augment the
existing model to achieve improved performance at the task of object
recognition. Transferred fusion learning is one such mechanism that intends to
use the best of both worlds and build a model that is capable of outperforming
the models involved in the system. We propose a novel mechanism to amplify the
process of transfer learning by introducing a student architecture where the
networks learn from each other.
