{"id":"https://openalex.org/W3097305019","doi":"https://doi.org/10.1145/3423457.3429361","title":"VAE-Info-cGAN","display_name":"VAE-Info-cGAN","publication_year":2020,"publication_date":"2020-11-03","ids":{"openalex":"https://openalex.org/W3097305019","doi":"https://doi.org/10.1145/3423457.3429361","mag":"3097305019"},"language":"en","primary_location":{"id":"doi:10.1145/3423457.3429361","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3423457.3429361","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 13th ACM SIGSPATIAL International Workshop on Computational Transportation Science","raw_type":"proceedings-article"},"type":"preprint","indexed_in":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2012.04196","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5020783183","display_name":"Xue\u2010Rong Xiao","orcid":"https://orcid.org/0000-0002-3926-8325"},"institutions":[{"id":"https://openalex.org/I97018004","display_name":"Stanford University","ror":"https://ror.org/00f54p054","country_code":"US","type":"education","lineage":["https://openalex.org/I97018004"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Xuerong Xiao","raw_affiliation_strings":["Stanford University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Stanford University","institution_ids":["https://openalex.org/I97018004"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5050692100","display_name":"Swetava Ganguli","orcid":"https://orcid.org/0000-0002-7392-4867"},"institutions":[{"id":"https://openalex.org/I4210107260","display_name":"Apple (United Kingdom)","ror":"https://ror.org/01vpeym60","country_code":"GB","type":"company","lineage":["https://openalex.org/I4210107260"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Swetava Ganguli","raw_affiliation_strings":["Apple"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Apple","institution_ids":["https://openalex.org/I4210107260"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5038217225","display_name":"Vipul Pandey","orcid":"https://orcid.org/0000-0003-2063-8645"},"institutions":[{"id":"https://openalex.org/I4210107260","display_name":"Apple (United Kingdom)","ror":"https://ror.org/01vpeym60","country_code":"GB","type":"company","lineage":["https://openalex.org/I4210107260"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Vipul Pandey","raw_affiliation_strings":["Apple"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Apple","institution_ids":["https://openalex.org/I4210107260"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.5873,"has_fulltext":false,"cited_by_count":9,"citation_normalized_percentile":{"value":0.7,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"10"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.9940000176429749,"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/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.9940000176429749,"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":0.9836999773979187,"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/T11164","display_name":"Remote Sensing and LiDAR Applications","score":0.9478999972343445,"subfield":{"id":"https://openalex.org/subfields/2305","display_name":"Environmental Engineering"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental 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":0.7413777112960815},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6975650787353516},{"id":"https://openalex.org/keywords/autoencoder","display_name":"Autoencoder","score":0.6832271218299866},{"id":"https://openalex.org/keywords/feature-vector","display_name":"Feature vector","score":0.6018986701965332},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5359545350074768},{"id":"https://openalex.org/keywords/mnist-database","display_name":"MNIST database","score":0.47485044598579407},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4606819748878479},{"id":"https://openalex.org/keywords/exploit","display_name":"Exploit","score":0.45441967248916626},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.44546687602996826},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.42583024501800537},{"id":"https://openalex.org/keywords/generative-grammar","display_name":"Generative grammar","score":0.42445603013038635},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.3786601424217224},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.32199734449386597}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7413777112960815},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6975650787353516},{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.6832271218299866},{"id":"https://openalex.org/C83665646","wikidata":"https://www.wikidata.org/wiki/Q42139305","display_name":"Feature vector","level":2,"score":0.6018986701965332},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5359545350074768},{"id":"https://openalex.org/C190502265","wikidata":"https://www.wikidata.org/wiki/Q17069496","display_name":"MNIST database","level":3,"score":0.47485044598579407},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4606819748878479},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.45441967248916626},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.44546687602996826},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.42583024501800537},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.42445603013038635},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.3786601424217224},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.32199734449386597},{"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},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1145/3423457.3429361","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3423457.3429361","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 13th ACM SIGSPATIAL International Workshop on Computational Transportation Science","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:2012.04196","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2012.04196","pdf_url":"https://arxiv.org/pdf/2012.04196","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":"acceptedVersion","is_accepted":true,"is_published":false,"raw_source_name":null,"raw_type":"text"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2012.04196","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2012.04196","pdf_url":"https://arxiv.org/pdf/2012.04196","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":"acceptedVersion","is_accepted":true,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":29,"referenced_works":["https://openalex.org/W1988580225","https://openalex.org/W2099057450","https://openalex.org/W2125389028","https://openalex.org/W2188365844","https://openalex.org/W2434741482","https://openalex.org/W2464624156","https://openalex.org/W2535388113","https://openalex.org/W2744091666","https://openalex.org/W2783496839","https://openalex.org/W2807692250","https://openalex.org/W2893749619","https://openalex.org/W2898091194","https://openalex.org/W2910577860","https://openalex.org/W2914312101","https://openalex.org/W2939016802","https://openalex.org/W2951786554","https://openalex.org/W2952716587","https://openalex.org/W2962770929","https://openalex.org/W2962793481","https://openalex.org/W2963226019","https://openalex.org/W2964167449","https://openalex.org/W2964350391","https://openalex.org/W2984529706","https://openalex.org/W3034431451","https://openalex.org/W4251629533","https://openalex.org/W4288581795","https://openalex.org/W4288586763","https://openalex.org/W4295112158","https://openalex.org/W4313342210"],"related_works":["https://openalex.org/W4386603768","https://openalex.org/W2950475743","https://openalex.org/W4394785709","https://openalex.org/W4296978181","https://openalex.org/W2912987408","https://openalex.org/W2937381246","https://openalex.org/W3004801820","https://openalex.org/W4281672036","https://openalex.org/W4313444753","https://openalex.org/W4230582276"],"abstract_inverted_index":{"Training":[0],"robust":[1],"supervised":[2],"deep":[3,89],"learning":[4],"models":[5],"for":[6,29,110,274,278],"many":[7,30],"geospatial":[8,286],"applications":[9,31],"of":[10,18,70,80,163,171,201,243,257,265,282],"computer":[11,279],"vision":[12],"is":[13,32,143,152,183,190,215,268],"difficult":[14],"due":[15],"to":[16,77,145,176,203,285],"dearth":[17],"class-balanced":[19],"and":[20,65,122,142,288],"diverse":[21],"training":[22,27,218],"data.":[23,82],"Conversely,":[24],"obtaining":[25],"enough":[26],"data":[28,51,270,276],"financially":[33],"prohibitive":[34],"or":[35,46],"may":[36],"be":[37,73,146],"infeasible,":[38],"especially":[39],"when":[40],"the":[41,67,129,135,139,160,164,169,177,194,224,235,258,266],"application":[42,264],"involves":[43],"modeling":[44,281],"rare":[45],"extreme":[47],"events.":[48],"Synthetically":[49],"generating":[50],"(and":[52,271],"labels)":[53],"using":[54],"a":[55,62,88,97,102,118,123,147,182,202,210,219,229,254],"generative":[56,91],"model":[57,237],"that":[58,95,234],"can":[59,72,131,238],"sample":[60],"from":[61,138,185,193],"target":[63,178],"distribution":[64],"exploit":[66],"multi-scale":[68],"nature":[69],"images":[71,114,207],"an":[74,155],"inexpensive":[75],"solution":[76],"address":[78],"scarcity":[79],"labeled":[81],"Towards":[83],"this":[84],"goal,":[85],"we":[86],"present":[87],"conditional":[90,103],"model,":[92],"called":[93],"VAE-Info-cGAN,":[94],"combines":[96],"Variational":[98],"Autoencoder":[99],"(VAE)":[100],"with":[101],"Information":[104],"Maximizing":[105],"Generative":[106],"Adversarial":[107],"Network":[108],"(InfoGAN),":[109],"synthesizing":[111],"semantically":[112],"rich":[113],"simultaneously":[115],"conditioned":[116,251],"on":[117,228,253],"pixel-level":[119],"condition":[120,126],"(PLC)":[121],"macroscopic":[124,213],"feature-level":[125],"(FLC).":[127],"Dimensionally,":[128],"PLC":[130],"only":[132,252],"vary":[133],"in":[134,159,223],"channel":[136],"dimension":[137],"synthesized":[140],"image":[141,166],"meant":[144],"task-specific":[148],"input.":[149],"The":[150,261],"FLC":[151],"modeled":[153],"as":[154],"attribute":[156],"vector,":[157],"a,":[158],"latent":[161,225],"space":[162],"generated":[165],"which":[167],"controls":[168],"contributions":[170],"various":[172,241],"characteristic":[173],"attributes":[174],"germane":[175],"distribution.":[179],"During":[180],"generation,":[181],"sampled":[184],"U[0,":[186],"1],":[187],"while":[188,250],"it":[189],"learned":[191],"directly":[192],"ground":[195],"truth":[196],"during":[197],"training.":[198],"An":[199],"interpretation":[200],"systematically":[204],"generate":[205,240],"synthetic":[206,269],"by":[208,217],"varying":[209],"chosen":[211],"binary":[212,221],"feature":[214],"explored":[216],"linear":[220],"classifier":[222],"space.":[226],"Experiments":[227],"GPS":[230],"trajectories":[231],"dataset":[232],"show":[233],"proposed":[236],"accurately":[239],"forms":[242],"spatio-temporal":[244],"aggregates":[245],"across":[246],"different":[247],"geographic":[248],"locations":[249],"raster":[255],"representation":[256],"road":[259],"network.":[260],"primary":[262],"intended":[263],"VAE-Info-cGAN":[267],"label)":[272],"generation":[273],"targeted":[275],"augmentation":[277],"vision-based":[280],"problems":[283],"relevant":[284],"analysis":[287],"remote":[289],"sensing.":[290]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":4}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2020-11-09T00:00:00"}