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Feature-Based Interpretation of the Deep Neural Network

Authors
Lee, Eun-HunKim, Hyeoncheol
Issue Date
11월-2021
Publisher
MDPI
Keywords
explainable artificial intelligence (XAI); interpretability; neural network
Citation
ELECTRONICS, v.10, no.21
Indexed
SCIE
SCOPUS
Journal Title
ELECTRONICS
Volume
10
Number
21
URI
https://scholar.korea.ac.kr/handle/2021.sw.korea/135876
DOI
10.3390/electronics10212687
ISSN
2079-9292
Abstract
The significant advantage of deep neural networks is that the upper layer can capture the high-level features of data based on the information acquired from the lower layer by stacking layers deeply. Since it is challenging to interpret what knowledge the neural network has learned, various studies for explaining neural networks have emerged to overcome this problem. However, these studies generate the local explanation of a single instance rather than providing a generalized global interpretation of the neural network model itself. To overcome such drawbacks of the previous approaches, we propose the global interpretation method for the deep neural network through features of the model. We first analyzed the relationship between the input and hidden layers to represent the high-level features of the model, then interpreted the decision-making process of neural networks through high-level features. In addition, we applied network pruning techniques to make concise explanations and analyzed the effect of layer complexity on interpretability. We present experiments on the proposed approach using three different datasets and show that our approach could generate global explanations on deep neural network models with high accuracy and fidelity.
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