Quantifying Layerwise Information Discarding of Neural Networks.
Year: 2019
Type: preprint
Abstract: This paper presents a method to explain how input information is discarded through intermediate layers of a neural network during the forward propagation, in order to quantify and diagnose knowledge representations of pre-trained deep neural networks. We define two types of entropy-based metrics, i.e., the strict information discarding and the reconstruction uncertainty, which measure input information of a specific layer from two perspectives. We develop a method to enable efficient computation of such entropy-based metrics. Our method can be broadly applied to various neural networks and enable comprehensive comparisons between different layers of different networks. Preliminary experiments have shown the effectiveness of our metrics in analyzing benchmark networks and explaining existing deep-learning techniques.
Source: arXiv (Cornell University)
Authors: Haotian Ma, Yinqing Zhang, Fan Zhou, Quanshi Zhang
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W2949017160