  Science is used to discover the law of world. Machine learning can be used to
discover the law of data. In recent years, there are more and more research
about interpretability in machine learning community. We hope the machine
learning methods are safe, interpretable, and they can help us to find
meaningful pattern in data. In this paper, we focus on interpretability of deep
representation. We propose a interpretable method of representation based on
mutual information, which summarizes the interpretation of representation into
three types of information between input data and representation. We further
proposed MI-LR module, which can be inserted into the model to estimate the
amount of information to explain the model's representation. Finally, we verify
the method through the visualization of the prototype network.
