Interpretable Neural Network Decoupling

Understand

The remarkable performance of convolutional neural networks (CNNs) is entangled with their huge number of uninterpretable parameters, which has become the bottleneck limiting the exploitation of their full potential.

• Towards network interpretation, previous endeavors mainly resort to the single filter analysis, which however ignores the relationship between filters.
• In this paper, we propose a novel architecture decoupling method to interpret the network from a perspective of investigating its calculation paths.
• More specifically, we introduce a novel architecture controlling module in each layer to encode the network architecture by a vector.