An Introduction to Deep Visual Explanation

Understand

The practical impact of deep learning on complex supervised learning problems has been significant, so much so that almost every Artificial Intelligence problem, or at least a portion thereof, has been somehow recast as a deep learning problem.

• The applications appeal is significant, but this appeal is increasingly challenged by what some call the challenge of explainability, or more generally the more traditional challenge of debuggability: if the outcomes of a deep learning process produce unexpected results (e.g., less than expected performance of a classifier), then there is little available in the way of theories or tools to help investigate the potential causes of such unexpected behavior, especially when this behavior could impact people's lives.
• We describe a preliminary framework to help address this issue, which we call "deep visual explanation" (DVE).
• "Deep," because it is the development and performance of deep neural network models that we want to understand.