论文标题
CNN图像分类器中的信号强度和噪声驱动器特征偏好
Signal Strength and Noise Drive Feature Preference in CNN Image Classifiers
论文作者
论文摘要
卷积神经网络（CNN）图像分类器中的特征偏好是其决策过程不可或缺的一部分，尽管对该主题进行了充分的研究，但在基本层面上仍然尚不清楚。我们在高度控制的CNN图像分类实验中使用合成数据集测试了一系列与任务相关的特征属性（包括形状，纹理和颜色），并使用合成数据集进行不同程度的信号和噪声来确定特征偏好。我们发现，CNN会更喜欢具有更强信号强度和较低噪声的功能，而不论特征是纹理，形状还是颜色。这为任务相关功能偏好的预测模型提供了指导，可以通过对实验设置进行仔细的控制来避免使用机器模型中的偏差途径，并建议应对人类和机器之间的偏爱任务分类任务中的任务相关特征进行比较。可以在\ url {https://github.com/mwolff31/signal_preference}找到本文中重现实验的代码。
Feature preference in Convolutional Neural Network (CNN) image classifiers is integral to their decision making process, and while the topic has been well studied, it is still not understood at a fundamental level. We test a range of task relevant feature attributes (including shape, texture, and color) with varying degrees of signal and noise in highly controlled CNN image classification experiments using synthetic datasets to determine feature preferences. We find that CNNs will prefer features with stronger signal strength and lower noise irrespective of whether the feature is texture, shape, or color. This provides guidance for a predictive model for task relevant feature preferences, demonstrates pathways for bias in machine models that can be avoided with careful controls on experimental setup, and suggests that comparisons between how humans and machines prefer task relevant features in vision classification tasks should be revisited. Code to reproduce experiments in this paper can be found at \url{https://github.com/mwolff31/signal_preference}.
