理解深度卷积网络。
Understanding deep convolutional networks.
作者信息
Mallat Stéphane
机构信息
École Normale Supérieure, CNRS, PSL, 45 rue d'Ulm, Paris, France
出版信息
Philos Trans A Math Phys Eng Sci. 2016 Apr 13;374(2065):20150203. doi: 10.1098/rsta.2015.0203.
Abstract
Deep convolutional networks provide state-of-the-art classifications and regressions results over many high-dimensional problems. We review their architecture, which scatters data with a cascade of linear filter weights and nonlinearities. A mathematical framework is introduced to analyse their properties. Computations of invariants involve multiscale contractions with wavelets, the linearization of hierarchical symmetries and sparse separations. Applications are discussed.
摘要
深度卷积网络在许多高维问题上提供了最先进的分类和回归结果。我们回顾了它们的架构，该架构通过一系列线性滤波器权重和非线性函数来分散数据。引入了一个数学框架来分析它们的属性。不变量的计算涉及小波的多尺度收缩、层次对称性的线性化和稀疏分离。文中还讨论了相关应用。
相似文献
1
Understanding deep convolutional networks.
Philos Trans A Math Phys Eng Sci. 2016 Apr 13;374(2065):20150203. doi: 10.1098/rsta.2015.0203.
2
Hierarchical Recurrent Neural Hashing for Image Retrieval With Hierarchical Convolutional Features.
IEEE Trans Image Process. 2018;27(1):106-120. doi: 10.1109/TIP.2017.2755766.
3
Deep convolutional neural networks for automatic classification of gastric carcinoma using whole slide images in digital histopathology.
Comput Med Imaging Graph. 2017 Nov;61:2-13. doi: 10.1016/j.compmedimag.2017.06.001. Epub 2017 Jun 16.
4
A novel end-to-end classifier using domain transferred deep convolutional neural networks for biomedical images.
Comput Methods Programs Biomed. 2017 Mar;140:283-293. doi: 10.1016/j.cmpb.2016.12.019. Epub 2017 Jan 6.
5
Towards dropout training for convolutional neural networks.
Neural Netw. 2015 Nov;71:1-10. doi: 10.1016/j.neunet.2015.07.007. Epub 2015 Jul 29.
6
Deep Convolutional Neural Networks for large-scale speech tasks.
Neural Netw. 2015 Apr;64:39-48. doi: 10.1016/j.neunet.2014.08.005. Epub 2014 Sep 16.
7
Sensor Fusion for Myoelectric Control Based on Deep Learning With Recurrent Convolutional Neural Networks.
Artif Organs. 2018 Sep;42(9):E272-E282. doi: 10.1111/aor.13153. Epub 2018 Jul 13.
8
Brain tumor segmentation with Deep Neural Networks.
Med Image Anal. 2017 Jan;35:18-31. doi: 10.1016/j.media.2016.05.004. Epub 2016 May 19.
9
Fast learning method for convolutional neural networks using extreme learning machine and its application to lane detection.
Neural Netw. 2017 Mar;87:109-121. doi: 10.1016/j.neunet.2016.12.002. Epub 2016 Dec 10.
10
Stacked Convolutional Denoising Auto-Encoders for Feature Representation.
IEEE Trans Cybern. 2017 Apr;47(4):1017-1027. doi: 10.1109/TCYB.2016.2536638. Epub 2016 Mar 16.
引用本文的文献
1
A model for tobacco growing area classification based on time series features of thermogravimetric analysis.
Biotechnol Biofuels Bioprod. 2025 Aug 11;18(1):90. doi: 10.1186/s13068-025-02682-x.
2
A novel approach to graph distinction through GENEOs and permutants.
Sci Rep. 2025 Feb 20;15(1):6259. doi: 10.1038/s41598-025-90152-7.
3
Investigating the Effect of Vibration Signal Length on Bearing Fault Classification Using Wavelet Scattering Transform.
Sensors (Basel). 2025 Jan 24;25(3):699. doi: 10.3390/s25030699.
4
From motion to emotion: exploring challenging behaviors in autism spectrum disorder through analysis of wearable physiology and movement.
Physiol Meas. 2025 Jan 29;13(1):015004. doi: 10.1088/1361-6579/ada51b.
5
Harnessing Deep Learning for Accurate Pathological Assessment of Brain Tumor Cell Types.
J Imaging Inform Med. 2025 Apr;38(2):1098-1111. doi: 10.1007/s10278-024-01107-9. Epub 2024 Aug 16.
6
Wavelet scattering networks in deep learning for discovering protein markers in a cohort of Swedish rectal cancer patients.
Cancer Med. 2023 Dec;12(23):21502-21518. doi: 10.1002/cam4.6672. Epub 2023 Nov 28.
7
Functional Connectivity and Feature Fusion Enhance Multiclass Motor-Imagery Brain-Computer Interface Performance.
Sensors (Basel). 2023 Aug 30;23(17):7520. doi: 10.3390/s23177520.
8
Covariance properties under natural image transformations for the generalised Gaussian derivative model for visual receptive fields.
Front Comput Neurosci. 2023 Jun 15;17:1189949. doi: 10.3389/fncom.2023.1189949. eCollection 2023.
9
Development of Machine-Learning Models for Tinnitus-Related Distress Classification Using Wavelet-Transformed Auditory Evoked Potential Signals and Clinical Data.
J Clin Med. 2023 Jun 4;12(11):3843. doi: 10.3390/jcm12113843.
10
[Primary study on recognition of vascular stiffness based on wavelet scattering neural network].
Sheng Wu Yi Xue Gong Cheng Xue Za Zhi. 2023 Apr 25;40(2):244-248. doi: 10.7507/1001-5515.202207068.
本文引用的文献
1
Deep learning.
Nature. 2015 May 28;521(7553):436-44. doi: 10.1038/nature14539.
2
Deep learning of the tissue-regulated splicing code.
Bioinformatics. 2014 Jun 15;30(12):i121-9. doi: 10.1093/bioinformatics/btu277.
3
Invariant scattering convolution networks.
IEEE Trans Pattern Anal Mach Intell. 2013 Aug;35(8):1872-86. doi: 10.1109/TPAMI.2012.230.
Zotero 插件