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arXiv · 2401.09518

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

Euna Jung·Jaekeol Choi·EungGu Yun·Wonjong Rhee
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Abstract

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 \textit{On-Off pattern} and \textit{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.

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Euna Jung, Jaekeol Choi, EungGu Yun, Wonjong Rhee. 2024-01-17. On-Off Pattern Encoding and Path-Count Encoding as Deep Neural Network Representations. https://arxiv.org/abs/2401.09518

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