  In recent years, deep learning has become prevalent to solve applications
from multiple domains. Convolutional Neural Networks (CNNs) particularly have
demonstrated state of the art performance for the task of image classification.
However, the decisions made by these networks are not transparent and cannot be
directly interpreted by a human. Several approaches have been proposed to
explain to understand the reasoning behind a prediction made by a network. In
this paper, we propose a topology of grouping these methods based on their
assumptions and implementations. We focus primarily on white box methods that
leverage the information of the internal architecture of a network to explain
its decision. Given the task of image classification and a trained CNN, this
work aims to provide a comprehensive and detailed overview of a set of methods
that can be used to create explanation maps for a particular image, that assign
an importance score to each pixel of the image based on its contribution to the
decision of the network. We also propose a further classification of the white
box methods based on their implementations to enable better comparisons and
help researchers find methods best suited for different scenarios.
