Using KL-divergence to focus Deep Visual Explanation

Understand

We present a method for explaining the image classification predictions of deep convolution neural networks, by highlighting the pixels in the image which influence the final class prediction.

• Our method requires the identification of a heuristic method to select parameters hypothesized to be most relevant in this prediction, and here we use Kullback-Leibler divergence to provide this focus.
• Overall, our approach helps in understanding and interpreting deep network predictions and we hope contributes to a foundation for such understanding of deep learning networks.
• In this brief paper, our experiments evaluate the performance of two popular networks in this context of interpretability.