{"id":"https://openalex.org/W2592600582","doi":"https://doi.org/10.1109/icassp.2017.7952676","title":"Learning rotation invariance in deep hierarchies using circular symmetric filters","display_name":"Learning rotation invariance in deep hierarchies using circular symmetric filters","publication_year":2017,"publication_date":"2017-03-01","ids":{"openalex":"https://openalex.org/W2592600582","doi":"https://doi.org/10.1109/icassp.2017.7952676","mag":"2592600582"},"language":"en","primary_location":{"id":"doi:10.1109/icassp.2017.7952676","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp.2017.7952676","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5073381340","display_name":"Dhruv Kohli","orcid":"https://orcid.org/0000-0002-0306-913X"},"institutions":[{"id":"https://openalex.org/I4210139030","display_name":"Samsung (India)","ror":"https://ror.org/04cpx2569","country_code":"IN","type":"company","lineage":["https://openalex.org/I2250650973","https://openalex.org/I4210139030"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Dhruv Kohli","raw_affiliation_strings":["Samsung R&D Institute, Bangalore, India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Samsung R&D Institute, Bangalore, India","institution_ids":["https://openalex.org/I4210139030"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5033240798","display_name":"Biplab Chandra Das","orcid":"https://orcid.org/0009-0003-8669-5033"},"institutions":[{"id":"https://openalex.org/I4210139030","display_name":"Samsung (India)","ror":"https://ror.org/04cpx2569","country_code":"IN","type":"company","lineage":["https://openalex.org/I2250650973","https://openalex.org/I4210139030"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Biplab Ch Das","raw_affiliation_strings":["Samsung R&D Institute, Bangalore, India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Samsung R&D Institute, Bangalore, India","institution_ids":["https://openalex.org/I4210139030"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5044982683","display_name":"Viswanath Gopalakrishnan","orcid":"https://orcid.org/0000-0001-7813-877X"},"institutions":[{"id":"https://openalex.org/I4210139030","display_name":"Samsung (India)","ror":"https://ror.org/04cpx2569","country_code":"IN","type":"company","lineage":["https://openalex.org/I2250650973","https://openalex.org/I4210139030"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Viswanath Gopalakrishnan","raw_affiliation_strings":["Samsung R&D Institute, Bangalore, India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Samsung R&D Institute, Bangalore, India","institution_ids":["https://openalex.org/I4210139030"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5112206030","display_name":"Kiran Nanjunda Iyer","orcid":null},"institutions":[{"id":"https://openalex.org/I4210139030","display_name":"Samsung (India)","ror":"https://ror.org/04cpx2569","country_code":"IN","type":"company","lineage":["https://openalex.org/I2250650973","https://openalex.org/I4210139030"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Kiran Nanjunda Iyer","raw_affiliation_strings":["Samsung R&D Institute, Bangalore, India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Samsung R&D Institute, Bangalore, India","institution_ids":["https://openalex.org/I4210139030"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I4210139030"],"apc_list":null,"apc_paid":null,"fwci":0.2771,"has_fulltext":false,"cited_by_count":6,"citation_normalized_percentile":{"value":0.62360265,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":"abs/1202.2745","issue":null,"first_page":"2846","last_page":"2850"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.9991000294685364,"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"}},"topics":[{"id":"https://openalex.org/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.9991000294685364,"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/T14339","display_name":"Image Processing and 3D Reconstruction","score":0.9980999827384949,"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/T10191","display_name":"Robotics and Sensor-Based Localization","score":0.9980000257492065,"subfield":{"id":"https://openalex.org/subfields/2202","display_name":"Aerospace Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/rotation","display_name":"Rotation (mathematics)","score":0.704922616481781},{"id":"https://openalex.org/keywords/mnist-database","display_name":"MNIST database","score":0.638559103012085},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6318895816802979},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.6232221126556396},{"id":"https://openalex.org/keywords/symmetry","display_name":"Symmetry (geometry)","score":0.5724592804908752},{"id":"https://openalex.org/keywords/constraint","display_name":"Constraint (computer-aided design)","score":0.5346172451972961},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5161322355270386},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5127599835395813},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4962330460548401},{"id":"https://openalex.org/keywords/convolution","display_name":"Convolution (computer science)","score":0.47838345170021057},{"id":"https://openalex.org/keywords/rotational-invariance","display_name":"Rotational invariance","score":0.4648377597332001},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.41519176959991455},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.4097435772418976},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.3526188135147095},{"id":"https://openalex.org/keywords/geometry","display_name":"Geometry","score":0.19531109929084778}],"concepts":[{"id":"https://openalex.org/C74050887","wikidata":"https://www.wikidata.org/wiki/Q848368","display_name":"Rotation (mathematics)","level":2,"score":0.704922616481781},{"id":"https://openalex.org/C190502265","wikidata":"https://www.wikidata.org/wiki/Q17069496","display_name":"MNIST database","level":3,"score":0.638559103012085},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6318895816802979},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.6232221126556396},{"id":"https://openalex.org/C2779886137","wikidata":"https://www.wikidata.org/wiki/Q21030012","display_name":"Symmetry (geometry)","level":2,"score":0.5724592804908752},{"id":"https://openalex.org/C2776036281","wikidata":"https://www.wikidata.org/wiki/Q48769818","display_name":"Constraint (computer-aided design)","level":2,"score":0.5346172451972961},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5161322355270386},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5127599835395813},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4962330460548401},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.47838345170021057},{"id":"https://openalex.org/C33098168","wikidata":"https://www.wikidata.org/wiki/Q108537107","display_name":"Rotational invariance","level":2,"score":0.4648377597332001},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.41519176959991455},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.4097435772418976},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3526188135147095},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.19531109929084778},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icassp.2017.7952676","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp.2017.7952676","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":13,"referenced_works":["https://openalex.org/W1522301498","https://openalex.org/W1904365287","https://openalex.org/W1994197834","https://openalex.org/W2025183033","https://openalex.org/W2040679279","https://openalex.org/W2097117768","https://openalex.org/W2141125852","https://openalex.org/W2157629193","https://openalex.org/W2163605009","https://openalex.org/W2340427832","https://openalex.org/W2618530766","https://openalex.org/W2964121744","https://openalex.org/W4301045096"],"related_works":["https://openalex.org/W2950475743","https://openalex.org/W4386603768","https://openalex.org/W2886711096","https://openalex.org/W4380078352","https://openalex.org/W3046591097","https://openalex.org/W2590796488","https://openalex.org/W2734358244","https://openalex.org/W2750384547","https://openalex.org/W2809732489","https://openalex.org/W2987170380"],"abstract_inverted_index":{"Deep":[0],"hierarchical":[1],"models":[2,26],"for":[3,12],"feature":[4,92,103],"learning":[5],"have":[6,27],"emerged":[7],"as":[8],"an":[9],"effective":[10],"technique":[11,61],"object":[13],"representation":[14],"and":[15,50,138],"classification":[16],"in":[17,76,87,106],"recent":[18],"years.":[19],"Though":[20],"the":[21,42,54,68,77,102,107,119,152],"features":[22],"learnt":[23],"using":[24,45],"deep":[25,69,129],"shown":[28],"lot":[29],"of":[30,44,90,101],"promise":[31],"towards":[32],"achieving":[33],"invariance":[34,95,124],"to":[35,62,82],"data":[36,49,137],"transformations,":[37],"this":[38],"primarily":[39],"comes":[40],"at":[41],"expense":[43],"much":[46,134],"larger":[47],"training":[48,67,120,136],"model":[51,70,139],"size.":[52],"In":[53],"proposed":[55],"work":[56],"we":[57],"devise":[58],"a":[59],"novel":[60],"incorporate":[63],"rotation":[64,123],"invariance,":[65],"while":[66],"parameters.":[71,140],"The":[72],"convolution":[73],"weight":[74],"parameters":[75],"network":[78,131],"architecture":[79],"are":[80],"constrained":[81],"exhibit":[83],"circular":[84,115],"symmetry":[85,116],"resulting":[86],"\u201crotation":[88],"equivariance\u201d":[89],"output":[91],"maps.":[93],"Rotation":[94],"is":[96],"further":[97,149],"achieved":[98,127],"by":[99,113],"max-pooling":[100],"maps":[104],"later":[105],"hierarchy.":[108],"We":[109],"also":[110],"show":[111],"that":[112],"incorporating":[114],"constraint":[117],"into":[118],"loss":[121],"function,":[122],"can":[125],"be":[126],"with-in":[128],"neural":[130],"framework":[132],"with":[133],"lesser":[135],"Our":[141],"experiment":[142],"results":[143],"evaluated":[144],"on":[145],"rotated":[146],"MNIST":[147],"dataset":[148],"objectively":[150],"validate":[151],"contribution.":[153]},"counts_by_year":[{"year":2021,"cited_by_count":3},{"year":2020,"cited_by_count":1},{"year":2018,"cited_by_count":2}],"updated_date":"2026-06-26T08:34:08.712188","created_date":"2025-10-10T00:00:00"}