  We deal with the problem of semantic classification of challenging and
highly-cluttered dataset. We present a novel, and yet a very simple
classification technique by leveraging the ease of classifiability of any
existing well separable dataset for guidance. Since the guide dataset which may
or may not have any semantic relationship with the experimental dataset, forms
well separable clusters in the feature set, the proposed network tries to embed
class-wise features of the challenging dataset to those distinct clusters of
the guide set, making them more separable. Depending on the availability, we
propose two types of guide sets: one using texture (image) guides and another
using prototype vectors representing cluster centers. Experimental results
obtained on the challenging benchmark RSSCN, LSUN, and TU-Berlin datasets
establish the efficacy of the proposed method as we outperform the existing
state-of-the-art techniques by a considerable margin.
