图像错误分类的因果解释

图像分类错误的因果解释是一个未被充分研究的领域，它可以为模型的可解释性提供潜在的有价值的见解，并提高预测的准确性。这项研究在六个现代CNN架构上训练CIFAR-10，包括VGG16，ResNet50，GoogLeNet，DenseNet161，MobileNet V2和Inception V3，并使用条件混淆矩阵和错误分类网络探索错误分类模式。..

Causal Explanations of Image Misclassifications

The causal explanation of image misclassifications is an understudied niche, which can potentially provide valuable insights in model interpretability and increase prediction accuracy. This study trains CIFAR-10 on six modern CNN architectures, including VGG16, ResNet50, GoogLeNet, DenseNet161, MobileNet V2, and Inception V3, and explores the misclassification patterns using conditional confusion matrices and misclassification networks.Two causes are identified and qualitatively distinguished: morphological similarity and non-essential information interference. The former cause is not model dependent, whereas the latter is inconsistent across all six models. To reduce the misclassifications caused by non-essential information interference, this study erases the pixels within the bonding boxes anchored at the top 5% pixels of the saliency map. This method first verifies the cause; then by directly modifying the cause it reduces the misclassification. Future studies will focus on quantitatively differentiating the two causes of misclassifications, generalizing the anchor-box based inference modification method to reduce misclassification, exploring the interactions of the two causes in misclassifications.