  Deep convolutional neural networks (CNN) have revolutionized various fields
of vision research and have seen unprecedented adoption for multiple tasks such
as classification, detection, captioning, etc. However, they offer little
transparency into their inner workings and are often treated as black boxes
that deliver excellent performance. In this work, we aim at alleviating this
opaqueness of CNNs by providing visual explanations for the network's
predictions. Our approach can analyze variety of CNN based models trained for
vision applications such as object recognition and caption generation. Unlike
existing methods, we achieve this via unraveling the forward pass operation.
Proposed method exploits feature dependencies across the layer hierarchy and
uncovers the discriminative image locations that guide the network's
predictions. We name these locations CNN-Fixations, loosely analogous to human
eye fixations.
  Our approach is a generic method that requires no architectural changes,
additional training or gradient computation and computes the important image
locations (CNN Fixations). We demonstrate through a variety of applications
that our approach is able to localize the discriminative image locations across
different network architectures, diverse vision tasks and data modalities.
