  Convolution Neural Networks is one of the most powerful tools in the present
era of science. There has been a lot of research done to improve their
performance and robustness while their internal working was left unexplored to
much extent. They are often defined as black boxes that can map non-linear data
very effectively. This paper tries to show how CNN has learned to look at an
image. The proposed algorithm exploits the basic math of CNN to backtrack the
important pixels it is considering to predict. This is a simple algorithm which
does not involve any training of its own over a pre-trained CNN which can
classify.
