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We show that early layers do\nnot necessarily need to be learned, providing the best results to-date with\npre-defined representations while being competitive with Deep CNNs. Using a\nshallow cascade of 1 x 1 convolutions, which encodes scattering coefficients\nthat correspond to spatial windows of very small sizes, permits to obtain\nAlexNet accuracy on the imagenet ILSVRC2012. We demonstrate that this local\nencoding explicitly learns invariance w.r.t. rotations. Combining scattering\nnetworks with a modern ResNet, we achieve a single-crop top 5 error of 11.4% on\nimagenet ILSVRC2012, comparable to the Resnet-18 architecture, while utilizing\nonly 10 layers. We also find that hybrid architectures can yield excellent\nperformance in the small sample regime, exceeding their end-to-end\ncounterparts, through their ability to incorporate geometrical priors. We\ndemonstrate this on subsets of the CIFAR-10 dataset and on the STL-10 dataset.","url_abs":"http://arxiv.org/abs/1703.08961v2","url_pdf":"http://arxiv.org/pdf/1703.08961v2.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":"scaling-the-scattering-transform-deep-hybrid","repo_url":"https://github.com/edouardoyallon/pyscatwave","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"scaling-the-scattering-transform-deep-hybrid","repo_url":"https://github.com/edouardoyallon/scalingscattering","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}}],"tasks":[{"task_slug":"image-classification","task_name":"Image Classification"}],"methods":[{"method_slug":"1x1-convolution","method_name":"1x1 Convolution"},{"method_slug":"average-pooling","method_name":"Average Pooling"},{"method_slug":"batch-normalization","method_name":"Batch Normalization"},{"method_slug":"bottleneck-residual-block","method_name":"Bottleneck Residual Block"},{"method_slug":"convolution","method_name":"Convolution"},{"method_slug":"global-average-pooling","method_name":"Global Average Pooling"},{"method_slug":"kaiming-initialization","method_name":"Kaiming Initialization"},{"method_slug":"max-pooling","method_name":"Max Pooling"},{"method_slug":"relu","method_name":"ReLU"},{"method_slug":"residual-block","method_name":"Residual Block"},{"method_slug":"residual-connection","method_name":"Residual Connection"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/image-classification-on-stl-10","task":"Image Classification","dataset":"STL-10","model":"Scat + WRN 20-8","rank_in_archive_order":72,"of":117,"metrics":{"Percentage correct":"76.6"},"uses_additional_data":false},{"leaderboard":"/sota/image-classification-on-stl-10","task":"Image Classification","dataset":"STL-10","model":"Exemplar CNN","rank_in_archive_order":74,"of":117,"metrics":{"Percentage correct":"75.7"},"uses_additional_data":false},{"leaderboard":"/sota/image-classification-on-stl-10","task":"Image Classification","dataset":"STL-10","model":"Stacked what-where AE","rank_in_archive_order":76,"of":117,"metrics":{"Percentage correct":"74.33"},"uses_additional_data":false},{"leaderboard":"/sota/image-classification-on-stl-10","task":"Image Classification","dataset":"STL-10","model":"CNN","rank_in_archive_order":90,"of":117,"metrics":{"Percentage correct":"70.7"},"uses_additional_data":false},{"leaderboard":"/sota/image-classification-on-stl-10","task":"Image Classification","dataset":"STL-10","model":"Hierarchical Matching Pursuit (HMP)","rank_in_archive_order":98,"of":117,"metrics":{"Percentage correct":"64.6"},"uses_additional_data":false},{"leaderboard":"/sota/image-classification-on-stl-10","task":"Image Classification","dataset":"STL-10","model":"Convolutional K-means Network","rank_in_archive_order":105,"of":117,"metrics":{"Percentage correct":"60.2"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1703.08961","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1703.08961"}},"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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