论文标题
使用小波和光谱方法研究图像分类数据集中的模式
Using Wavelets and Spectral Methods to Study Patterns in Image-Classification Datasets
论文作者
论文摘要
深度学习模型提取在最终分类层之前提取的功能或模式，这是其前所未有的有利性能的关键。但是，复杂的非线性特征提取的过程尚不清楚，这是解释，对抗性鲁棒性和深神经网的概括的主要原因，都是开放的研究问题。在本文中，我们使用小波转换和光谱方法来分析图像分类数据集的内容，从数据集中提取特定模式，并找到模式和类之间的关联。我们表明，每个图像都可以写入小波空间中有限数量的等级-1模式的总结，从而提供了较低的等级近似值，从而捕获了学习所必需的结构和模式。关于记忆与学习的研究，当图像被随机标记时，我们的结果清楚地揭示了类模式与类别的分离。我们的方法可以用作一种模式识别方法来理解和解释这些数据集的可学习性。它也可以用于获得有关深层分类器从数据集中学习的功能和模式的见解。
Deep learning models extract, before a final classification layer, features or patterns which are key for their unprecedented advantageous performance. However, the process of complex nonlinear feature extraction is not well understood, a major reason why interpretation, adversarial robustness, and generalization of deep neural nets are all open research problems. In this paper, we use wavelet transformation and spectral methods to analyze the contents of image classification datasets, extract specific patterns from the datasets and find the associations between patterns and classes. We show that each image can be written as the summation of a finite number of rank-1 patterns in the wavelet space, providing a low rank approximation that captures the structures and patterns essential for learning. Regarding the studies on memorization vs learning, our results clearly reveal disassociation of patterns from classes, when images are randomly labeled. Our method can be used as a pattern recognition approach to understand and interpret learnability of these datasets. It may also be used for gaining insights about the features and patterns that deep classifiers learn from the datasets.
