Demystifying Deep Neural Networks Through Interpretation: A Survey

Understand

Modern deep learning algorithms tend to optimize an objective metric, such as minimize a cross entropy loss on a training dataset, to be able to learn.

• The problem is that the single metric is an incomplete description of the real world tasks.
• The single metric cannot explain why the algorithm learn.
• When an erroneous happens, the lack of interpretability causes a hardness of understanding and fixing the error.