{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/invariant-scattering-convolution-networks","title":"Invariant Scattering Convolution Networks","arxiv_id":"1203.1513","date":"2012-03-05","proceeding":null,"authors":["Joan Bruna","Stéphane Mallat"],"abstract":"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 non-linear modulus and averaging operators. The first network layer outputs SIFT-type descriptors whereas the next layers provide complementary invariant information which 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, using a Gaussian kernel SVM and a generative PCA classifier.","url_abs":"https://arxiv.org/abs/1203.1513v2","url_pdf":"https://arxiv.org/pdf/1203.1513v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"invariant-scattering-convolution-networks","repo_url":"https://github.com/fbcotter/scatnet_learn","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"classification-1","task_name":"Classification"},{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"translation","task_name":"Translation"}],"methods":[{"method_slug":"convolution","method_name":"Convolution"},{"method_slug":"pca","method_name":"PCA"},{"method_slug":"svm","method_name":"SVM"},{"method_slug":"scatnet","method_name":"ScatNet"}],"datasets_introduced":[],"methods_introduced":[{"slug":"scatnet","name":"ScatNet","full_name":"Scattering Transform"}],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1203.1513","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1203.1513"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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