{"id":"https://openalex.org/W3025009576","doi":"https://doi.org/10.1109/wacv45572.2020.9093327","title":"SketchTransfer: A Challenging New Task for Exploring Detail-Invariance and the Abstractions Learned by Deep Networks","display_name":"SketchTransfer: A Challenging New Task for Exploring Detail-Invariance and the Abstractions Learned by Deep Networks","publication_year":2020,"publication_date":"2020-03-01","ids":{"openalex":"https://openalex.org/W3025009576","doi":"https://doi.org/10.1109/wacv45572.2020.9093327","mag":"3025009576"},"language":"en","primary_location":{"id":"doi:10.1109/wacv45572.2020.9093327","is_oa":false,"landing_page_url":"https://doi.org/10.1109/wacv45572.2020.9093327","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE Winter Conference on Applications of Computer Vision (WACV)","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/A5030891292","display_name":"Alex Lamb","orcid":null},"institutions":[{"id":"https://openalex.org/I4210155582","display_name":"Centre Universitaire de Mila","ror":"https://ror.org/05s3cw058","country_code":"DZ","type":"education","lineage":["https://openalex.org/I4210155582"]}],"countries":["DZ"],"is_corresponding":true,"raw_author_name":"Alex Lamb","raw_affiliation_strings":["MILA"],"affiliations":[{"raw_affiliation_string":"MILA","institution_ids":["https://openalex.org/I4210155582"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5049332971","display_name":"Sherjil Ozair","orcid":null},"institutions":[{"id":"https://openalex.org/I4210155582","display_name":"Centre Universitaire de Mila","ror":"https://ror.org/05s3cw058","country_code":"DZ","type":"education","lineage":["https://openalex.org/I4210155582"]}],"countries":["DZ"],"is_corresponding":false,"raw_author_name":"Sherjil Ozair","raw_affiliation_strings":["MILA"],"affiliations":[{"raw_affiliation_string":"MILA","institution_ids":["https://openalex.org/I4210155582"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5083701330","display_name":"Vikas Verma","orcid":"https://orcid.org/0000-0002-4911-4709"},"institutions":[{"id":"https://openalex.org/I9927081","display_name":"Aalto University","ror":"https://ror.org/020hwjq30","country_code":"FI","type":"education","lineage":["https://openalex.org/I9927081"]}],"countries":["FI"],"is_corresponding":false,"raw_author_name":"Vikas Verma","raw_affiliation_strings":["MILA, Aalto University, Finland"],"affiliations":[{"raw_affiliation_string":"MILA, Aalto University, Finland","institution_ids":["https://openalex.org/I9927081"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5001419875","display_name":"David Ha","orcid":"https://orcid.org/0000-0001-8765-8574"},"institutions":[{"id":"https://openalex.org/I1291425158","display_name":"Google (United States)","ror":"https://ror.org/00njsd438","country_code":"US","type":"company","lineage":["https://openalex.org/I1291425158","https://openalex.org/I4210128969"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"David Ha","raw_affiliation_strings":["Google, Brain"],"affiliations":[{"raw_affiliation_string":"Google, Brain","institution_ids":["https://openalex.org/I1291425158"]}]}],"institutions":[],"countries_distinct_count":3,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5030891292"],"corresponding_institution_ids":["https://openalex.org/I4210155582"],"apc_list":null,"apc_paid":null,"fwci":0.7954,"has_fulltext":false,"cited_by_count":6,"citation_normalized_percentile":{"value":0.77853726,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":"17","issue":null,"first_page":"952","last_page":"961"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.9998000264167786,"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/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.9998000264167786,"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/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/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.9995999932289124,"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":0.7700668573379517},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.7309372425079346},{"id":"https://openalex.org/keywords/human\u2013computer-interaction","display_name":"Human\u2013computer interaction","score":0.40631964802742004},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.40479809045791626},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.06259027123451233},{"id":"https://openalex.org/keywords/systems-engineering","display_name":"Systems engineering","score":0.06139582395553589}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7700668573379517},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.7309372425079346},{"id":"https://openalex.org/C107457646","wikidata":"https://www.wikidata.org/wiki/Q207434","display_name":"Human\u2013computer interaction","level":1,"score":0.40631964802742004},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.40479809045791626},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.06259027123451233},{"id":"https://openalex.org/C201995342","wikidata":"https://www.wikidata.org/wiki/Q682496","display_name":"Systems engineering","level":1,"score":0.06139582395553589}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/wacv45572.2020.9093327","is_oa":false,"landing_page_url":"https://doi.org/10.1109/wacv45572.2020.9093327","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE Winter Conference on Applications of Computer Vision (WACV)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/4","score":0.5699999928474426,"display_name":"Quality Education"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":74,"referenced_works":["https://openalex.org/W1673923490","https://openalex.org/W1731081199","https://openalex.org/W1837544984","https://openalex.org/W1924619199","https://openalex.org/W1945616565","https://openalex.org/W1972420097","https://openalex.org/W1997876580","https://openalex.org/W2431080869","https://openalex.org/W2466618734","https://openalex.org/W2606712314","https://openalex.org/W2765407302","https://openalex.org/W2786559811","https://openalex.org/W2788590143","https://openalex.org/W2795456585","https://openalex.org/W2890208753","https://openalex.org/W2902986194","https://openalex.org/W2909190397","https://openalex.org/W2910992787","https://openalex.org/W2921320475","https://openalex.org/W2921861056","https://openalex.org/W2936017220","https://openalex.org/W2940550985","https://openalex.org/W2944141891","https://openalex.org/W2948467580","https://openalex.org/W2951970475","https://openalex.org/W2952610664","https://openalex.org/W2962793481","https://openalex.org/W2962808524","https://openalex.org/W2962970380","https://openalex.org/W2963207607","https://openalex.org/W2963399829","https://openalex.org/W2963435192","https://openalex.org/W2963920537","https://openalex.org/W2964153729","https://openalex.org/W2964159205","https://openalex.org/W2974342968","https://openalex.org/W2976016473","https://openalex.org/W2976023236","https://openalex.org/W2978426779","https://openalex.org/W3004377989","https://openalex.org/W3006025420","https://openalex.org/W3034175346","https://openalex.org/W3127433878","https://openalex.org/W3209458476","https://openalex.org/W4288366733","https://openalex.org/W4297772692","https://openalex.org/W4300833946","https://openalex.org/W4308831279","https://openalex.org/W6637162671","https://openalex.org/W6637618735","https://openalex.org/W6638515385","https://openalex.org/W6640174519","https://openalex.org/W6640425456","https://openalex.org/W6717772578","https://openalex.org/W6736562241","https://openalex.org/W6745136726","https://openalex.org/W6746282794","https://openalex.org/W6748312029","https://openalex.org/W6748976676","https://openalex.org/W6749725282","https://openalex.org/W6754184789","https://openalex.org/W6756444276","https://openalex.org/W6758132781","https://openalex.org/W6758204279","https://openalex.org/W6760184523","https://openalex.org/W6760606919","https://openalex.org/W6760851457","https://openalex.org/W6761338119","https://openalex.org/W6762710920","https://openalex.org/W6762823943","https://openalex.org/W6762913911","https://openalex.org/W6764051988","https://openalex.org/W6768312477","https://openalex.org/W6768501859"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2748952813","https://openalex.org/W2390279801","https://openalex.org/W2358668433","https://openalex.org/W4396701345","https://openalex.org/W2376932109","https://openalex.org/W2001405890","https://openalex.org/W4396696052","https://openalex.org/W4402327032","https://openalex.org/W3196817267"],"abstract_inverted_index":{"Deep":[0],"networks":[1],"have":[2,34],"achieved":[3],"excellent":[4],"results":[5],"in":[6,110],"perceptual":[7],"tasks,":[8],"yet":[9,75],"their":[10,31],"ability":[11,32],"to":[12,14,33,48,61,79,103,130],"generalize":[13],"variations":[15],"not":[16],"seen":[17],"during":[18],"training":[19],"has":[20,212],"come":[21],"under":[22],"increasing":[23],"scrutiny.":[24],"In":[25],"this":[26,105,155,203],"work":[27],"we":[28,145],"focus":[29],"on":[30,164,173,197],"invariance":[35],"towards":[36],"the":[37,116,132,174,189],"presence":[38],"or":[39],"absence":[40],"of":[41,98,118,188,192],"details.":[42],"For":[43],"example,":[44,66],"humans":[45],"are":[46,52,77,87],"able":[47,78],"watch":[49],"cartoons,":[50],"which":[51,160,177],"missing":[53,88],"many":[54],"visual":[55],"details,":[56],"without":[57],"being":[58],"explicitly":[59],"trained":[60,195],"do":[62,104],"so.":[63],"As":[64],"another":[65],"3D":[67],"rendering":[68],"software":[69],"is":[70,178,205],"a":[71,91,107,123,138,193],"relatively":[72],"recent":[73],"development,":[74],"people":[76],"understand":[80],"such":[81],"rendered":[82],"scenes":[83],"even":[84],"though":[85],"they":[86],"details":[89],"(consider":[90],"film":[92],"like":[93],"Toy":[94],"Story).":[95],"The":[96,157],"failure":[97],"ma-":[99],"chine":[100],"learning":[101],"algorithms":[102,151],"indicates":[106,201],"significant":[108],"gap":[109],"generalization":[111],"between":[112],"human":[113],"abilities":[114,117],"and":[115,144],"deep":[119],"networks.":[120],"We":[121,136],"propose":[122],"dataset":[124],"that":[125,147,202],"will":[126],"make":[127],"it":[128],"easier":[129],"study":[131],"detail-invariance":[133],"problem":[134],"concretely.":[135],"produce":[137],"concrete":[139],"task":[140,204],"for":[141,215],"this:":[142],"SketchTransfer,":[143],"show":[146],"state-of-the-art":[148,158],"domain":[149],"transfer":[150,168],"still":[152],"struggle":[153],"with":[154,207],"task.":[156],"technique":[159],"achieves":[161,170],"over":[162],"95%":[163],"MNIST":[165],"\u2192":[166],"SVHN":[167],"only":[169],"59%":[171],"accuracy":[172,191],"SketchTransfer":[175],"task,":[176],"much":[179],"better":[180],"than":[181],"random":[182],"(11%":[183],"accuracy)":[184],"but":[185,211],"falls":[186],"short":[187],"87%":[190],"classifier":[194],"directly":[196],"labeled":[198],"sketches.":[199],"This":[200],"approachable":[206],"today\u2019s":[208],"best":[209],"methods":[210],"substantial":[213],"room":[214],"improvement.":[216]},"counts_by_year":[{"year":2022,"cited_by_count":3},{"year":2021,"cited_by_count":2},{"year":2020,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}