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Self-explaining models are models that reveal decision making parameters inan interpretable manner so that the model reasoning process can be directlyunderstood by human beings. General Linear Models (GLMs) are self-explainingbecause the model weights directly show how each feature contributes to theoutput value. However, deep neural networks (DNNs) are in general notself-explaining due to the non-linearity of the activation functions, complexarchitectures, obscure feature extraction and transformation process. In thiswork, we illustrate the fact that existing deep architectures are hard tointerpret because each hidden layer carries a mix of low level features andhigh level features. As a solution, we propose a novel feature levelingarchitecture that isolates low level features from high level features on aper-layer basis to better utilize the GLM layer in the proposed architecturefor interpretation. Experimental results show that our modified models are ableto achieve competitive results comparing to main-stream architectures onstandard datasets while being more self-explainable. Our implementations andconfigurations are publicly available for reproductions

Not All Features Are Equal: Feature Leveling Deep Neural Networks for Better Interpretation