On-Off Pattern Encoding and Path-Count Encoding as Deep Neural Network Representations

Understanding the encoded representation of Deep Neural Networks (DNNs) has been a fundamental yet challenging objective. In this work, we focus on two possible directions for analyzing representations of DNNs by studying simple image classification tasks. Specifically, we consider $\mathit{\text{On-Off}}\mathit{\text{pattern}}$ and $\mathit{\text{PathCount}}$ for investigating how information is stored in deep representations. On-off pattern of a neuron is decided as on' or off’ depending on whether the neuron’s activation after ReLU is non-zero or zero. PathCount is the number of paths that transmit non-zero energy from the input to a neuron. We investigate how neurons in the network encodes information by replacing each layer’s activation with On-Off pattern or PathCount and evaluating its effect on classification performance. We also examine correlation between representation and PathCount. Finally, we show a possible way to improve an existing DNN interpretation method, Class Activation Map (CAM), by directly utilizing On-Off or PathCount.