{"id":"https://openalex.org/W2938837420","doi":"https://doi.org/10.1109/icassp.2019.8682192","title":"Learning Convolutional Neural Networks with Deep Part Embeddings","display_name":"Learning Convolutional Neural Networks with Deep Part Embeddings","publication_year":2019,"publication_date":"2019-04-17","ids":{"openalex":"https://openalex.org/W2938837420","doi":"https://doi.org/10.1109/icassp.2019.8682192","mag":"2938837420"},"language":"en","primary_location":{"id":"doi:10.1109/icassp.2019.8682192","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp.2019.8682192","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ICASSP 2019 - 2019 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/A5054069765","display_name":"Nitin Gupta","orcid":"https://orcid.org/0000-0003-0177-6292"},"institutions":[{"id":"https://openalex.org/I4210103279","display_name":"IBM Research - India","ror":"https://ror.org/014wt7r80","country_code":"IN","type":"facility","lineage":["https://openalex.org/I1341412227","https://openalex.org/I4210103279","https://openalex.org/I4210114115"]}],"countries":["IN"],"is_corresponding":true,"raw_author_name":"Nitin Gupta","raw_affiliation_strings":["IBM Research Laboratory, India"],"affiliations":[{"raw_affiliation_string":"IBM Research Laboratory, India","institution_ids":["https://openalex.org/I4210103279"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5054372047","display_name":"Shashank Mujumdar","orcid":null},"institutions":[{"id":"https://openalex.org/I4210103279","display_name":"IBM Research - India","ror":"https://ror.org/014wt7r80","country_code":"IN","type":"facility","lineage":["https://openalex.org/I1341412227","https://openalex.org/I4210103279","https://openalex.org/I4210114115"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Shashank Mujumdar","raw_affiliation_strings":["IBM Research Laboratory, India"],"affiliations":[{"raw_affiliation_string":"IBM Research Laboratory, India","institution_ids":["https://openalex.org/I4210103279"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5039639106","display_name":"Prerna Agarwal","orcid":"https://orcid.org/0000-0003-0338-3679"},"institutions":[{"id":"https://openalex.org/I4210103279","display_name":"IBM Research - India","ror":"https://ror.org/014wt7r80","country_code":"IN","type":"facility","lineage":["https://openalex.org/I1341412227","https://openalex.org/I4210103279","https://openalex.org/I4210114115"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Prerna Agarwal","raw_affiliation_strings":["IBM Research Laboratory, India"],"affiliations":[{"raw_affiliation_string":"IBM Research Laboratory, India","institution_ids":["https://openalex.org/I4210103279"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5033509566","display_name":"Abhinav Jain","orcid":"https://orcid.org/0000-0003-3094-4603"},"institutions":[{"id":"https://openalex.org/I4210103279","display_name":"IBM Research - India","ror":"https://ror.org/014wt7r80","country_code":"IN","type":"facility","lineage":["https://openalex.org/I1341412227","https://openalex.org/I4210103279","https://openalex.org/I4210114115"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Abhinav Jain","raw_affiliation_strings":["IBM Research Laboratory, India"],"affiliations":[{"raw_affiliation_string":"IBM Research Laboratory, India","institution_ids":["https://openalex.org/I4210103279"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5046646390","display_name":"Sameep Mehta","orcid":"https://orcid.org/0000-0002-9599-1526"},"institutions":[{"id":"https://openalex.org/I4210103279","display_name":"IBM Research - India","ror":"https://ror.org/014wt7r80","country_code":"IN","type":"facility","lineage":["https://openalex.org/I1341412227","https://openalex.org/I4210103279","https://openalex.org/I4210114115"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Sameep Mehta","raw_affiliation_strings":["IBM Research Laboratory, India"],"affiliations":[{"raw_affiliation_string":"IBM Research Laboratory, India","institution_ids":["https://openalex.org/I4210103279"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5054069765"],"corresponding_institution_ids":["https://openalex.org/I4210103279"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.03114821,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"2037","last_page":"2041"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.9997000098228455,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.9997000098228455,"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/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.9986000061035156,"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/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8503921031951904},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.7583863735198975},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7451760768890381},{"id":"https://openalex.org/keywords/class","display_name":"Class (philosophy)","score":0.716050922870636},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.641904354095459},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4719585180282593},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.46196410059928894},{"id":"https://openalex.org/keywords/filter","display_name":"Filter (signal processing)","score":0.4602164030075073},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.45979899168014526},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4216781258583069},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.052839696407318115}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8503921031951904},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.7583863735198975},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7451760768890381},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.716050922870636},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.641904354095459},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4719585180282593},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.46196410059928894},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.4602164030075073},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.45979899168014526},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4216781258583069},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.052839696407318115}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icassp.2019.8682192","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp.2019.8682192","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ICASSP 2019 - 2019 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":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":34,"referenced_works":["https://openalex.org/W118823016","https://openalex.org/W1576445103","https://openalex.org/W1821462560","https://openalex.org/W1825675169","https://openalex.org/W1849277567","https://openalex.org/W2031489346","https://openalex.org/W2062118960","https://openalex.org/W2104769604","https://openalex.org/W2108598243","https://openalex.org/W2120501001","https://openalex.org/W2145276819","https://openalex.org/W2149676790","https://openalex.org/W2155541015","https://openalex.org/W2161381512","https://openalex.org/W2168356304","https://openalex.org/W2788388592","https://openalex.org/W2803023299","https://openalex.org/W2962965870","https://openalex.org/W2963140066","https://openalex.org/W2963628712","https://openalex.org/W2964222566","https://openalex.org/W4294375521","https://openalex.org/W6634343353","https://openalex.org/W6638389677","https://openalex.org/W6638523607","https://openalex.org/W6639204139","https://openalex.org/W6676089979","https://openalex.org/W6677850982","https://openalex.org/W6681613270","https://openalex.org/W6682778277","https://openalex.org/W6685444988","https://openalex.org/W6726275242","https://openalex.org/W6748778324","https://openalex.org/W6751751081"],"related_works":["https://openalex.org/W4293226380","https://openalex.org/W4375867731","https://openalex.org/W2611989081","https://openalex.org/W4226493464","https://openalex.org/W4312417841","https://openalex.org/W3133861977","https://openalex.org/W2951211570","https://openalex.org/W3103566983","https://openalex.org/W3029198973","https://openalex.org/W3000197790"],"abstract_inverted_index":{"We":[0,24,126],"propose":[1],"a":[2,28,31,45,48,56,79,102,110],"novel":[3],"concept":[4],"of":[5,30,38,47,64,93],"Deep":[6],"Part":[7],"Embeddings":[8],"(DPEs),":[9],"which":[10],"can":[11],"be":[12],"used":[13],"to":[14,100,108],"learn":[15,109],"new":[16,57,111],"Convolutional":[17],"Neural":[18],"Networks":[19],"(CNNs)":[20],"for":[21],"different":[22,66,130],"classes.":[23,125],"define":[25],"DPE":[26],"as":[27,44],"neuron":[29,52],"trained":[32,73,104],"CNN":[33,103],"along":[34],"with":[35,86,113],"its":[36,120],"network":[37,80,131],"filter":[39],"activations":[40],"that":[41,50,68,81],"is":[42,97],"interpretable":[43],"part":[46],"class":[49,58,84,112],"the":[51,62,83,98,123,129,139],"contributes":[53],"to.":[54],"Given":[55],"C,":[59,71],"we":[60],"explore":[61],"idea":[63],"combining":[65],"DPEs":[67],"intuitively":[69],"constitute":[70],"from":[72],"CNNs":[74],"(not":[75],"on":[76,105,122],"C),":[77],"into":[78],"learns":[82],"C":[85],"few":[87],"training":[88,115],"samples.":[89],"An":[90],"important":[91],"application":[92],"our":[94],"proposed":[95],"framework":[96],"ability":[99],"modify":[101],"n":[106,124],"classes":[107],"limited":[114],"data":[116],"without":[117],"significantly":[118],"affecting":[119],"performance":[121,137],"visually":[127],"illustrate":[128],"architectures":[132],"and":[133],"extensively":[134],"evaluate":[135],"their":[136],"against":[138],"baselines.":[140]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
