Courant Institute, New York University, 715 Broadway, New York, NY 10003, USA.
IEEE Trans Pattern Anal Mach Intell. 2013 Aug;35(8):1872-86. doi: 10.1109/TPAMI.2012.230.
A wavelet scattering network computes a translation invariant image representation which is stable to deformations and preserves high-frequency information for classification. It cascades wavelet transform convolutions with nonlinear modulus and averaging operators. The first network layer outputs SIFT-type descriptors, whereas the next layers provide complementary invariant information that improves classification. The mathematical analysis of wavelet scattering networks explains important properties of deep convolution networks for classification. A scattering representation of stationary processes incorporates higher order moments and can thus discriminate textures having the same Fourier power spectrum. State-of-the-art classification results are obtained for handwritten digits and texture discrimination, with a Gaussian kernel SVM and a generative PCA classifier.
小波散射网络计算了一种对变形具有稳定性且保留高频信息的平移不变图像表示，以便进行分类。它使用非线性模量和平均运算符级联小波变换卷积。第一层网络输出 SIFT 型描述符，而接下来的层则提供互补的不变信息，从而提高分类性能。小波散射网络的数学分析解释了用于分类的深度卷积网络的重要性质。平稳过程的散射表示形式包含更高阶矩，因此可以区分具有相同傅里叶功率谱的纹理。使用高斯核 SVM 和生成 PCA 分类器，对手写数字和纹理识别获得了最先进的分类结果。
