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DDMI: A Model Information Evaluation Method Based on Deep Dream
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DOI：10.1016/j.dcan.2025.03.008.png)
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Abstract
En 中文
In recent years, the Internet of Everything (IoE) has been developing rapidly; however, there are currently issues with efficiency and sustainability within IoE. To address this problem, model lightweighting can be employed by constraining the size of deep learning models, thereby reducing the demand for computational resources and ensuring the efficiency and sustainability of IoE devices. In this regard, we propose a model information evaluation method based on DeepDream. This method does not require real samples to participate; instead, it evaluates the importance of each neuron based on the model's own information. Additionally, we introduce a method for automatically selecting high-information neurons, which can identify neurons that have a significant impact on the model. We also present a visualization method for neuron class information, which can visualize information related to various classes within neurons. Through experiments, we demonstrate the effectiveness of the methods we have proposed.
Keywords:
Deep learning
Deep dream
Model information
IoE
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