{"id":"https://openalex.org/W3162346114","doi":"https://doi.org/10.1109/icassp39728.2021.9413451","title":"Sparsity Driven Latent Space Sampling for Generative Prior Based Compressive Sensing","display_name":"Sparsity Driven Latent Space Sampling for Generative Prior Based Compressive Sensing","publication_year":2021,"publication_date":"2021-05-13","ids":{"openalex":"https://openalex.org/W3162346114","doi":"https://doi.org/10.1109/icassp39728.2021.9413451","mag":"3162346114"},"language":"en","primary_location":{"id":"doi:10.1109/icassp39728.2021.9413451","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp39728.2021.9413451","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5017851329","display_name":"Vinayak Killedar","orcid":null},"institutions":[{"id":"https://openalex.org/I59270414","display_name":"Indian Institute of Science Bangalore","ror":"https://ror.org/04dese585","country_code":"IN","type":"education","lineage":["https://openalex.org/I59270414"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Vinayak Killedar","raw_affiliation_strings":["Indian Institute of Science, Bangalore, India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Indian Institute of Science, Bangalore, India","institution_ids":["https://openalex.org/I59270414"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5029545056","display_name":"Praveen Kumar Pokala","orcid":"https://orcid.org/0000-0001-5513-5891"},"institutions":[{"id":"https://openalex.org/I59270414","display_name":"Indian Institute of Science Bangalore","ror":"https://ror.org/04dese585","country_code":"IN","type":"education","lineage":["https://openalex.org/I59270414"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Praveen Kumar Pokala","raw_affiliation_strings":["Indian Institute of Science, Bangalore, India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Indian Institute of Science, Bangalore, India","institution_ids":["https://openalex.org/I59270414"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5005652970","display_name":"Chandra Sekhar Seelamantula","orcid":"https://orcid.org/0000-0001-9049-1912"},"institutions":[{"id":"https://openalex.org/I59270414","display_name":"Indian Institute of Science Bangalore","ror":"https://ror.org/04dese585","country_code":"IN","type":"education","lineage":["https://openalex.org/I59270414"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Chandra Sekhar Seelamantula","raw_affiliation_strings":["Indian Institute of Science, Bangalore, India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Indian Institute of Science, Bangalore, India","institution_ids":["https://openalex.org/I59270414"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I59270414"],"apc_list":null,"apc_paid":null,"fwci":1.1961,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.87551076,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":"70","issue":null,"first_page":"2895","last_page":"2899"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10500","display_name":"Sparse and Compressive Sensing Techniques","score":1.0,"subfield":{"id":"https://openalex.org/subfields/2206","display_name":"Computational Mechanics"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10500","display_name":"Sparse and Compressive Sensing Techniques","score":1.0,"subfield":{"id":"https://openalex.org/subfields/2206","display_name":"Computational Mechanics"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10860","display_name":"Speech and Audio Processing","score":0.9990000128746033,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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/T11739","display_name":"Microwave Imaging and Scattering Analysis","score":0.9970999956130981,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical 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/mnist-database","display_name":"MNIST database","score":0.8982634544372559},{"id":"https://openalex.org/keywords/compressed-sensing","display_name":"Compressed sensing","score":0.8316892385482788},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6080777645111084},{"id":"https://openalex.org/keywords/prior-probability","display_name":"Prior probability","score":0.5983743071556091},{"id":"https://openalex.org/keywords/space","display_name":"Space (punctuation)","score":0.527116596698761},{"id":"https://openalex.org/keywords/sampling","display_name":"Sampling (signal processing)","score":0.5230751633644104},{"id":"https://openalex.org/keywords/generator","display_name":"Generator (circuit theory)","score":0.4886186718940735},{"id":"https://openalex.org/keywords/generative-model","display_name":"Generative model","score":0.4750368893146515},{"id":"https://openalex.org/keywords/latent-variable","display_name":"Latent variable","score":0.4600643217563629},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.42851153016090393},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4169631004333496},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.4146064817905426},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.33683258295059204},{"id":"https://openalex.org/keywords/generative-grammar","display_name":"Generative grammar","score":0.33531898260116577},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.14745119214057922},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.14228379726409912},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.10630637407302856}],"concepts":[{"id":"https://openalex.org/C190502265","wikidata":"https://www.wikidata.org/wiki/Q17069496","display_name":"MNIST database","level":3,"score":0.8982634544372559},{"id":"https://openalex.org/C124851039","wikidata":"https://www.wikidata.org/wiki/Q2665459","display_name":"Compressed sensing","level":2,"score":0.8316892385482788},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6080777645111084},{"id":"https://openalex.org/C177769412","wikidata":"https://www.wikidata.org/wiki/Q278090","display_name":"Prior probability","level":3,"score":0.5983743071556091},{"id":"https://openalex.org/C2778572836","wikidata":"https://www.wikidata.org/wiki/Q380933","display_name":"Space (punctuation)","level":2,"score":0.527116596698761},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.5230751633644104},{"id":"https://openalex.org/C2780992000","wikidata":"https://www.wikidata.org/wiki/Q17016113","display_name":"Generator (circuit theory)","level":3,"score":0.4886186718940735},{"id":"https://openalex.org/C167966045","wikidata":"https://www.wikidata.org/wiki/Q5532625","display_name":"Generative model","level":3,"score":0.4750368893146515},{"id":"https://openalex.org/C51167844","wikidata":"https://www.wikidata.org/wiki/Q4422623","display_name":"Latent variable","level":2,"score":0.4600643217563629},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.42851153016090393},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4169631004333496},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.4146064817905426},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.33683258295059204},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.33531898260116577},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.14745119214057922},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.14228379726409912},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.10630637407302856},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"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/C163258240","wikidata":"https://www.wikidata.org/wiki/Q25342","display_name":"Power (physics)","level":2,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/icassp39728.2021.9413451","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp39728.2021.9413451","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","raw_type":"proceedings-article"},{"id":"pmh:oai:eprints.iisc.ac.in:70208","is_oa":false,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4377196309","display_name":"NOT FOUND REPOSITORY (Indian Institute of Science Bangalore)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I59270414","host_organization_name":"Indian Institute of Science Bangalore","host_organization_lineage":["https://openalex.org/I59270414"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"acceptedVersion","is_accepted":true,"is_published":false,"raw_source_name":"","raw_type":"Conference Paper"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":42,"referenced_works":["https://openalex.org/W1959608418","https://openalex.org/W2099471712","https://openalex.org/W2100522270","https://openalex.org/W2100556411","https://openalex.org/W2112447569","https://openalex.org/W2119667497","https://openalex.org/W2135046866","https://openalex.org/W2147276092","https://openalex.org/W2220485312","https://openalex.org/W2296616510","https://openalex.org/W2503812215","https://openalex.org/W2595294663","https://openalex.org/W2604763608","https://openalex.org/W2619204584","https://openalex.org/W2785319177","https://openalex.org/W2787898208","https://openalex.org/W2798559986","https://openalex.org/W2809866471","https://openalex.org/W2917550189","https://openalex.org/W2938476239","https://openalex.org/W2945125725","https://openalex.org/W2950634328","https://openalex.org/W2962698501","https://openalex.org/W2963031612","https://openalex.org/W2963226019","https://openalex.org/W2967360854","https://openalex.org/W2998853723","https://openalex.org/W3012324700","https://openalex.org/W3090821698","https://openalex.org/W3100774664","https://openalex.org/W4250955649","https://openalex.org/W4301028776","https://openalex.org/W4320013936","https://openalex.org/W6640963894","https://openalex.org/W6676727762","https://openalex.org/W6718140377","https://openalex.org/W6735307612","https://openalex.org/W6736057607","https://openalex.org/W6748195391","https://openalex.org/W6752050450","https://openalex.org/W6752579191","https://openalex.org/W6777596526"],"related_works":["https://openalex.org/W2973021803","https://openalex.org/W4288257096","https://openalex.org/W2969856101","https://openalex.org/W3168675052","https://openalex.org/W4289105138","https://openalex.org/W4310699748","https://openalex.org/W2906272760","https://openalex.org/W2978098801","https://openalex.org/W4289694729","https://openalex.org/W3160820590"],"abstract_inverted_index":{"We":[0,55],"address":[1],"the":[2,44,53,66,70,81,100,105,111,114,118,137,140,147,155,166,174,182],"problem":[3],"of":[4,46,113,139,146,149,181],"recovering":[5,32],"signals":[6,33],"from":[7,34],"compressed":[8],"measurements":[9,150],"based":[10,16],"on":[11,125],"generative":[12],"priors.":[13],"Recently,":[14],"generative-model":[15],"compressive":[17,27],"sensing":[18,28],"(GMCS)":[19],"methods":[20],"have":[21],"shown":[22],"superior":[23],"performance":[24,45,138],"over":[25,117],"traditional":[26],"(CS)":[29],"techniques":[30,120],"in":[31,52,65,80,154,178,184],"fewer":[35],"measurements.":[36],"However,":[37],"it":[38],"is":[39,87],"possible":[40],"to":[41,62,76,123],"further":[42],"improve":[43],"GMCS":[47],"by":[48],"introducing":[49],"controlled":[50],"sparsity":[51,64,73,90,152,167],"latent-space.":[54],"propose":[56],"a":[57,77,144],"proximal":[58],"meta-learning":[59],"(PML)":[60],"algorithm":[61],"enforce":[63],"latent-space":[67],"while":[68],"training":[69],"generator.":[71],"Enforcing":[72],"naturally":[74],"leads":[75],"union-of-submanifolds":[78],"model":[79],"solution":[82],"space.":[83],"The":[84],"overall":[85],"framework":[86,116],"named":[88],"as":[89,129,143],"driven":[91,168],"latent":[92,156,169],"space":[93,157,170],"sampling":[94,171],"(SDLSS).":[95],"In":[96,133],"addition,":[97],"we":[98,109,135],"derive":[99],"sample":[101],"complexity":[102],"bounds":[103],"for":[104],"proposed":[106,115,141],"model.":[107],"Furthermore,":[108],"demonstrate":[110],"efficacy":[112],"state-of-the-art":[119],"with":[121],"application":[122],"CS":[124],"standard":[126,159],"datasets":[127],"such":[128],"MNIST":[130],"and":[131,151,176],"CIFAR-10.":[132],"particular,":[134],"evaluate":[136],"method":[142],"function":[145],"number":[148],"factor":[153],"using":[158],"objective":[160],"measures.":[161],"Our":[162],"findings":[163],"show":[164],"that":[165],"approach":[172],"improves":[173],"accuracy":[175],"aids":[177],"faster":[179],"recovery":[180],"signal":[183],"GMCS.":[185]},"counts_by_year":[{"year":2023,"cited_by_count":3},{"year":2022,"cited_by_count":1}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}