  In recent years neural networks have achieved state-of-the-art accuracy for
various tasks but the the interpretation of the generated outputs still remains
difficult. In this work we attempt to provide a method to understand the learnt
weights in the final fully connected layer in image classification models. We
motivate our method by drawing a connection between the policy gradient
objective in RL and supervised learning objective. We suggest that the commonly
used cross entropy based supervised learning objective can be regarded as a
special case of the policy gradient objective. Using this insight we propose a
method to find the most discriminative and confusing parts of an image. Our
method does not make any prior assumption about neural network achitecture and
has low computational cost. We apply our method on publicly available
pre-trained models and report the generated results.
