{"id":"https://openalex.org/W2922020726","doi":"https://doi.org/10.1109/icip.2019.8802977","title":"A Learnable Scatternet: Locally Invariant Convolutional Layers","display_name":"A Learnable Scatternet: Locally Invariant Convolutional Layers","publication_year":2019,"publication_date":"2019-08-26","ids":{"openalex":"https://openalex.org/W2922020726","doi":"https://doi.org/10.1109/icip.2019.8802977","mag":"2922020726"},"language":"en","primary_location":{"id":"doi:10.1109/icip.2019.8802977","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icip.2019.8802977","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE International Conference on Image Processing (ICIP)","raw_type":"proceedings-article"},"type":"preprint","indexed_in":["arxiv","crossref","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/1903.03137","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5084299875","display_name":"Fergal Cotter","orcid":null},"institutions":[{"id":"https://openalex.org/I241749","display_name":"University of Cambridge","ror":"https://ror.org/013meh722","country_code":"GB","type":"education","lineage":["https://openalex.org/I241749"]}],"countries":["GB"],"is_corresponding":true,"raw_author_name":"Fergal Cotter","raw_affiliation_strings":["University of Cambridge, Cambridge, UK","Univ. of Cambridge"],"affiliations":[{"raw_affiliation_string":"University of Cambridge, Cambridge, UK","institution_ids":["https://openalex.org/I241749"]},{"raw_affiliation_string":"Univ. of Cambridge","institution_ids":["https://openalex.org/I241749"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5110035142","display_name":"Nick Kingsbury","orcid":null},"institutions":[{"id":"https://openalex.org/I241749","display_name":"University of Cambridge","ror":"https://ror.org/013meh722","country_code":"GB","type":"education","lineage":["https://openalex.org/I241749"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Nick Kingsbury","raw_affiliation_strings":["University of Cambridge, Cambridge, UK","Univ. of Cambridge"],"affiliations":[{"raw_affiliation_string":"University of Cambridge, Cambridge, UK","institution_ids":["https://openalex.org/I241749"]},{"raw_affiliation_string":"Univ. of Cambridge","institution_ids":["https://openalex.org/I241749"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5084299875"],"corresponding_institution_ids":["https://openalex.org/I241749"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":true,"cited_by_count":1,"citation_normalized_percentile":{"value":0.02043832,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":null,"issue":null,"first_page":"350","last_page":"354"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10862","display_name":"AI in cancer detection","score":0.9980000257492065,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10862","display_name":"AI in cancer detection","score":0.9980000257492065,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9976000189781189,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.996999979019165,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6567502617835999},{"id":"https://openalex.org/keywords/invariant","display_name":"Invariant (physics)","score":0.6540434956550598},{"id":"https://openalex.org/keywords/scatternet","display_name":"Scatternet","score":0.5896886587142944},{"id":"https://openalex.org/keywords/tying","display_name":"Tying","score":0.5867846012115479},{"id":"https://openalex.org/keywords/scattering","display_name":"Scattering","score":0.5378498435020447},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5090255737304688},{"id":"https://openalex.org/keywords/asynchronous-communication","display_name":"Asynchronous communication","score":0.4357054829597473},{"id":"https://openalex.org/keywords/mixing","display_name":"Mixing (physics)","score":0.43452292680740356},{"id":"https://openalex.org/keywords/front","display_name":"Front (military)","score":0.42231059074401855},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3072904348373413},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.19556662440299988},{"id":"https://openalex.org/keywords/computer-network","display_name":"Computer network","score":0.18780571222305298},{"id":"https://openalex.org/keywords/optics","display_name":"Optics","score":0.14488232135772705},{"id":"https://openalex.org/keywords/telecommunications","display_name":"Telecommunications","score":0.09724009037017822}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6567502617835999},{"id":"https://openalex.org/C190470478","wikidata":"https://www.wikidata.org/wiki/Q2370229","display_name":"Invariant (physics)","level":2,"score":0.6540434956550598},{"id":"https://openalex.org/C252157","wikidata":"https://www.wikidata.org/wiki/Q348822","display_name":"Scatternet","level":4,"score":0.5896886587142944},{"id":"https://openalex.org/C2780938662","wikidata":"https://www.wikidata.org/wiki/Q973710","display_name":"Tying","level":2,"score":0.5867846012115479},{"id":"https://openalex.org/C191486275","wikidata":"https://www.wikidata.org/wiki/Q210028","display_name":"Scattering","level":2,"score":0.5378498435020447},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5090255737304688},{"id":"https://openalex.org/C151319957","wikidata":"https://www.wikidata.org/wiki/Q752739","display_name":"Asynchronous communication","level":2,"score":0.4357054829597473},{"id":"https://openalex.org/C138777275","wikidata":"https://www.wikidata.org/wiki/Q6884054","display_name":"Mixing (physics)","level":2,"score":0.43452292680740356},{"id":"https://openalex.org/C2777551076","wikidata":"https://www.wikidata.org/wiki/Q842332","display_name":"Front (military)","level":2,"score":0.42231059074401855},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3072904348373413},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.19556662440299988},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.18780571222305298},{"id":"https://openalex.org/C120665830","wikidata":"https://www.wikidata.org/wiki/Q14620","display_name":"Optics","level":1,"score":0.14488232135772705},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.09724009037017822},{"id":"https://openalex.org/C94523657","wikidata":"https://www.wikidata.org/wiki/Q4085781","display_name":"Wireless ad hoc network","level":3,"score":0.0},{"id":"https://openalex.org/C153294291","wikidata":"https://www.wikidata.org/wiki/Q25261","display_name":"Meteorology","level":1,"score":0.0},{"id":"https://openalex.org/C555944384","wikidata":"https://www.wikidata.org/wiki/Q249","display_name":"Wireless","level":2,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C37914503","wikidata":"https://www.wikidata.org/wiki/Q156495","display_name":"Mathematical physics","level":1,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0}],"mesh":[],"locations_count":4,"locations":[{"id":"doi:10.1109/icip.2019.8802977","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icip.2019.8802977","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE International Conference on Image Processing (ICIP)","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:1903.03137","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1903.03137","pdf_url":"https://arxiv.org/pdf/1903.03137","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"text"},{"id":"mag:2922020726","is_oa":true,"landing_page_url":"https://arxiv.org/pdf/1903.03137","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"arXiv (Cornell University)","raw_type":null},{"id":"doi:10.48550/arxiv.1903.03137","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.1903.03137","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:1903.03137","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1903.03137","pdf_url":"https://arxiv.org/pdf/1903.03137","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"text"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2922020726.pdf","grobid_xml":"https://content.openalex.org/works/W2922020726.grobid-xml"},"referenced_works_count":39,"referenced_works":["https://openalex.org/W1994906459","https://openalex.org/W2018332268","https://openalex.org/W2072072671","https://openalex.org/W2097117768","https://openalex.org/W2117539524","https://openalex.org/W2163605009","https://openalex.org/W2167215970","https://openalex.org/W2194775991","https://openalex.org/W2302255633","https://openalex.org/W2401231614","https://openalex.org/W2417429787","https://openalex.org/W2551176409","https://openalex.org/W2576915720","https://openalex.org/W2587914376","https://openalex.org/W2589078241","https://openalex.org/W2603142085","https://openalex.org/W2621199038","https://openalex.org/W2750651978","https://openalex.org/W2901247417","https://openalex.org/W2907824199","https://openalex.org/W2963076838","https://openalex.org/W2963382180","https://openalex.org/W2963408559","https://openalex.org/W2963446712","https://openalex.org/W2963754512","https://openalex.org/W2963857521","https://openalex.org/W2964118293","https://openalex.org/W2964137095","https://openalex.org/W6637373629","https://openalex.org/W6637551013","https://openalex.org/W6677651945","https://openalex.org/W6677995690","https://openalex.org/W6684191040","https://openalex.org/W6684563725","https://openalex.org/W6734131337","https://openalex.org/W6743833053","https://openalex.org/W6748243266","https://openalex.org/W6756329097","https://openalex.org/W6757754697"],"related_works":["https://openalex.org/W2970882063","https://openalex.org/W2738827206","https://openalex.org/W3118593905","https://openalex.org/W2738846774","https://openalex.org/W2788014331","https://openalex.org/W2798279435","https://openalex.org/W2766956386","https://openalex.org/W2795921906","https://openalex.org/W2564477780","https://openalex.org/W2982013607","https://openalex.org/W3173903754","https://openalex.org/W2905333383","https://openalex.org/W2363648501","https://openalex.org/W2898737887","https://openalex.org/W2996264629","https://openalex.org/W2471411992","https://openalex.org/W2798598284","https://openalex.org/W2922102348","https://openalex.org/W3197904945","https://openalex.org/W2766648902"],"abstract_inverted_index":{"In":[0],"this":[1,77,100],"paper":[2],"we":[3,88],"explore":[4],"tying":[5,28],"together":[6,30],"the":[7,38,43,73,81,130,146],"ideas":[8],"from":[9],"Scattering":[10],"Transforms":[11],"and":[12,50,93],"Convolutional":[13],"Neural":[14],"Networks":[15],"(CNN)":[16],"for":[17],"Image":[18],"Analysis":[19],"by":[20,78],"proposing":[21],"a":[22,46,51,54,95,117,120,149],"learnable":[23],"ScatterNet.":[24,74,121],"Previous":[25],"attempts":[26],"at":[27,60,145],"them":[29],"in":[31,128],"hybrid":[32],"networks":[33],"have":[34],"tended":[35],"to":[36,99,115],"keep":[37],"two":[39],"parts":[40],"separate,":[41],"with":[42],"ScatterNet":[44,131],"forming":[45,53],"fixed":[47],"front":[48,147],"end":[49],"CNN":[52,118],"learned":[55,70,96],"backend.":[56],"We":[57,75,122],"instead":[58],"look":[59],"adding":[61,69,94],"learning":[62,142],"between":[63],"scattering":[64,82],"orders,":[65],"as":[66,68],"well":[67],"layers":[71,87,109,140],"before":[72],"do":[76],"breaking":[79],"down":[80],"orders":[83],"into":[84],"single":[85],"convolutional-like":[86],"call":[89],"'locally":[90],"invariant'":[91],"layers,":[92],"mixing":[97],"term":[98],"layer.":[101],"Our":[102],"experiments":[103],"show":[104],"that":[105,129],"these":[106],"locally":[107],"invariant":[108],"can":[110],"improve":[111],"accuracy":[112],"when":[113],"added":[114],"either":[116],"or":[119,138],"also":[123],"discover":[124],"some":[125],"surprising":[126],"results":[127],"may":[132],"be":[133],"best":[134],"positioned":[135],"after":[136],"one":[137],"more":[139],"of":[141,148],"rather":[143],"than":[144],"neural":[150],"network.":[151]},"counts_by_year":[{"year":2023,"cited_by_count":1}],"updated_date":"2026-02-09T09:26:11.010843","created_date":"2025-10-10T00:00:00"}
