论文标题
可视化环氧树脂的X射线图像中的关键特征，以改善深度学习功能的单数值分解
Visualizing key features in X-ray images of epoxy resins for improved material classification using singular value decomposition of deep learning features
论文作者
论文摘要
尽管环氧树脂的过程变量改变了其机械性能，但这些材料样品X射线图像的特征的视觉识别是具有挑战性的。为了促进识别，我们近似不同类型的环氧树脂的X射线图像梯度的梯度的幅度，然后我们使用深度学习来发现转化图像的最具代表性的特征。在逆问题的解决方案中，找到特征特征以区分异质材料的样品，我们使用了从卷积神经网络中早期层的所有特征图的奇异值分解获得的特征向量。虽然最强的激活通道可视化特征特征，但在某些实际设置中通常不够健全。另一方面，特征图的矩阵分解的左单数向量，当变量（例如网络或网络体系结构的能力）发生变化时，几乎不会改变。这项工作介绍了高分类精度和特征特征的鲁棒性。
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.
