利用深度学习特征的奇异值分解可视化环氧树脂X射线图像中的关键特征以改进材料分类
Visualizing key features in X-ray images of epoxy resins for improved material classification using singular value decomposition of deep learning features
AI总结：
本文通过计算环氧树脂X射线图像强度梯度幅值，并利用卷积神经网络早期层特征图奇异值分解的左奇异向量提取稳定特征，实现了高精度且鲁棒的材料分类。
摘要：
尽管环氧树脂的工艺变量会改变其力学性能，但对这类材料样品的X射线图像中的特征进行视觉识别仍然具有挑战性。为了便于识别，我们首先近似计算不同种类环氧树脂X射线图像强度场的梯度幅值，然后利用深度学习发现变换后图像中最具代表性的特征。在求解这一用于区分异质材料样品的特征发现反问题时，我们使用卷积神经网络早期层特征图所有通道进行奇异值分解所得到的特征向量。虽然激活最强的通道能够给出特征的可视化表示，但在一些实际场景中这些表示往往不够鲁棒。另一方面，特征图矩阵分解的左奇异向量在诸如网络容量或网络架构等变量发生变化时几乎保持不变。本文展示了高分类精度以及特征表示的鲁棒性。
英文摘要：
Although the process variables of epoxy resins alter their mechanical properties, the visual identification of the characteristic features of X-ray images of samples of these materials is challenging. To facilitate the identification, we approximate the magnitude of the gradient of the intensity field of the X-ray images of different kinds of epoxy resins and then we use deep learning to discover the most representative features of the transformed images. In this solution of the inverse problem to finding characteristic features to discriminate samples of heterogeneous materials, we use the eigenvectors obtained from the singular value decomposition of all the channels of the feature maps of the early layers in a convolutional neural network. While the strongest activated channel gives a visual representation of the characteristic features, often these are not robust enough in some practical settings. On the other hand, the left singular vectors of the matrix decomposition of the feature maps, barely change when variables such as the capacity of the network or network architecture change. High classification accuracy and robustness of characteristic features are presented in this work.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
