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Jun, 2023
揭示模型偏差:通过采样还原分析评估深度神经网络
Revealing Model Biases: Assessing Deep Neural Networks via Recovered Sample Analysis
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Mohammad Mahdi Mehmanchi, Mahbod Nouri, Mohammad Sabokrou
TL;DR
本文提出了一种简单和经济有效的方法来评估深度神经网络是否依赖于训练样本的主要概念,或者只是学习区分类别的简单而无关紧要的特征。该方法通过恢复模型参数并分析重构质量来确定模型是否学习了所需的训练数据特征且不存在偏差。
Abstract
This paper proposes a straightforward and cost-effective approach to assess whether a
deep neural network
(DNN) relies on the
primary concepts
of training samples or simply learns discriminative, yet simple and i
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