{"id":"https://openalex.org/W1915485278","doi":"https://doi.org/10.1109/cvpr.2015.7299155","title":"Understanding deep image representations by inverting them","display_name":"Understanding deep image representations by inverting them","publication_year":2015,"publication_date":"2015-06-01","ids":{"openalex":"https://openalex.org/W1915485278","doi":"https://doi.org/10.1109/cvpr.2015.7299155","mag":"1915485278"},"language":"en","primary_location":{"id":"doi:10.1109/cvpr.2015.7299155","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cvpr.2015.7299155","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)","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/A5051934274","display_name":"Aravindh Mahendran","orcid":"https://orcid.org/0000-0002-2650-9871"},"institutions":[{"id":"https://openalex.org/I83328450","display_name":"Miami University","ror":"https://ror.org/05nbqxr67","country_code":"US","type":"education","lineage":["https://openalex.org/I83328450"]},{"id":"https://openalex.org/I40120149","display_name":"University of Oxford","ror":"https://ror.org/052gg0110","country_code":"GB","type":"education","lineage":["https://openalex.org/I40120149"]}],"countries":["GB","US"],"is_corresponding":true,"raw_author_name":"Aravindh Mahendran","raw_affiliation_strings":["University of Oxford","University of Oxford , USA"],"affiliations":[{"raw_affiliation_string":"University of Oxford","institution_ids":["https://openalex.org/I40120149"]},{"raw_affiliation_string":"University of Oxford , USA","institution_ids":["https://openalex.org/I83328450"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5060511349","display_name":"Andrea Vedaldi","orcid":"https://orcid.org/0000-0003-1374-2858"},"institutions":[{"id":"https://openalex.org/I83328450","display_name":"Miami University","ror":"https://ror.org/05nbqxr67","country_code":"US","type":"education","lineage":["https://openalex.org/I83328450"]},{"id":"https://openalex.org/I40120149","display_name":"University of Oxford","ror":"https://ror.org/052gg0110","country_code":"GB","type":"education","lineage":["https://openalex.org/I40120149"]}],"countries":["GB","US"],"is_corresponding":false,"raw_author_name":"Andrea Vedaldi","raw_affiliation_strings":["University of Oxford","University of Oxford , USA"],"affiliations":[{"raw_affiliation_string":"University of Oxford","institution_ids":["https://openalex.org/I40120149"]},{"raw_affiliation_string":"University of Oxford , USA","institution_ids":["https://openalex.org/I83328450"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5051934274"],"corresponding_institution_ids":["https://openalex.org/I40120149","https://openalex.org/I83328450"],"apc_list":null,"apc_paid":null,"fwci":96.7905,"has_fulltext":false,"cited_by_count":1862,"citation_normalized_percentile":{"value":0.99947471,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"5188","last_page":"5196"},"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":0.9997000098228455,"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":0.9997000098228455,"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/T10531","display_name":"Advanced Vision and Imaging","score":0.998199999332428,"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/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.9977999925613403,"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/convolutional-neural-network","display_name":"Convolutional neural network","score":0.7030602693557739},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7021213173866272},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.6863700151443481},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6343002319335938},{"id":"https://openalex.org/keywords/scale-invariant-feature-transform","display_name":"Scale-invariant feature transform","score":0.5497432947158813},{"id":"https://openalex.org/keywords/bag-of-words-model-in-computer-vision","display_name":"Bag-of-words model in computer vision","score":0.5358646512031555},{"id":"https://openalex.org/keywords/encoding","display_name":"Encoding (memory)","score":0.49817943572998047},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4803137183189392},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.4417153596878052},{"id":"https://openalex.org/keywords/visualization","display_name":"Visualization","score":0.41111502051353455},{"id":"https://openalex.org/keywords/visual-word","display_name":"Visual Word","score":0.35913610458374023},{"id":"https://openalex.org/keywords/image-retrieval","display_name":"Image retrieval","score":0.3097330331802368}],"concepts":[{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.7030602693557739},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7021213173866272},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.6863700151443481},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6343002319335938},{"id":"https://openalex.org/C61265191","wikidata":"https://www.wikidata.org/wiki/Q767770","display_name":"Scale-invariant feature transform","level":3,"score":0.5497432947158813},{"id":"https://openalex.org/C167611913","wikidata":"https://www.wikidata.org/wiki/Q6884747","display_name":"Bag-of-words model in computer vision","level":5,"score":0.5358646512031555},{"id":"https://openalex.org/C125411270","wikidata":"https://www.wikidata.org/wiki/Q18653","display_name":"Encoding (memory)","level":2,"score":0.49817943572998047},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4803137183189392},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4417153596878052},{"id":"https://openalex.org/C36464697","wikidata":"https://www.wikidata.org/wiki/Q451553","display_name":"Visualization","level":2,"score":0.41111502051353455},{"id":"https://openalex.org/C189391414","wikidata":"https://www.wikidata.org/wiki/Q7936579","display_name":"Visual Word","level":4,"score":0.35913610458374023},{"id":"https://openalex.org/C1667742","wikidata":"https://www.wikidata.org/wiki/Q10927554","display_name":"Image retrieval","level":3,"score":0.3097330331802368}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/cvpr.2015.7299155","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cvpr.2015.7299155","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)","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":52,"referenced_works":["https://openalex.org/W1484228140","https://openalex.org/W1528301850","https://openalex.org/W1551774182","https://openalex.org/W1625255723","https://openalex.org/W1673923490","https://openalex.org/W1849277567","https://openalex.org/W1972715665","https://openalex.org/W1976101156","https://openalex.org/W1982428585","https://openalex.org/W2009049007","https://openalex.org/W2012592962","https://openalex.org/W2025357764","https://openalex.org/W2027922120","https://openalex.org/W2079397195","https://openalex.org/W2095430846","https://openalex.org/W2100495367","https://openalex.org/W2104375222","https://openalex.org/W2117539524","https://openalex.org/W2124386111","https://openalex.org/W2131846894","https://openalex.org/W2134670479","https://openalex.org/W2147238549","https://openalex.org/W2147298660","https://openalex.org/W2151103935","https://openalex.org/W2154822588","https://openalex.org/W2154858868","https://openalex.org/W2159964742","https://openalex.org/W2161366920","https://openalex.org/W2161969291","https://openalex.org/W2163605009","https://openalex.org/W2168356304","https://openalex.org/W2171896402","https://openalex.org/W2952020226","https://openalex.org/W2962851944","https://openalex.org/W2962932385","https://openalex.org/W2963542991","https://openalex.org/W3207056347","https://openalex.org/W4388297464","https://openalex.org/W6629368666","https://openalex.org/W6631338704","https://openalex.org/W6633090903","https://openalex.org/W6636494156","https://openalex.org/W6637162671","https://openalex.org/W6639204139","https://openalex.org/W6675497075","https://openalex.org/W6677651945","https://openalex.org/W6679792166","https://openalex.org/W6681807782","https://openalex.org/W6684191040","https://openalex.org/W6685133223","https://openalex.org/W6685292306","https://openalex.org/W6802526406"],"related_works":["https://openalex.org/W2158102958","https://openalex.org/W2063218608","https://openalex.org/W1995462736","https://openalex.org/W4386105885","https://openalex.org/W2028757524","https://openalex.org/W2184288218","https://openalex.org/W2071180033","https://openalex.org/W2947282851","https://openalex.org/W3043037362","https://openalex.org/W2499029474"],"abstract_inverted_index":{"Image":[0],"representations,":[1],"from":[2],"SIFT":[3],"and":[4,143],"Bag":[5],"of":[6,18,27,39,54,111,141],"Visual":[7],"Words":[8],"to":[9,57,63,77,99,107],"Convolutional":[10],"Neural":[11],"Networks":[12],"(CNNs),":[13],"are":[14],"a":[15,36,74],"crucial":[16],"component":[17],"almost":[19],"any":[20],"image":[21,66,115],"understanding":[22,26],"system.":[23],"Nevertheless,":[24],"our":[25,122],"them":[28],"remains":[29],"limited.":[30],"In":[31],"this":[32,70,83,105],"paper":[33],"we":[34,72,124],"conduct":[35],"direct":[37],"analysis":[38],"the":[40,48,65,109,118,136],"visual":[41],"information":[42,134],"contained":[43],"in":[44,129],"representations":[45,87,116],"by":[46],"asking":[47],"following":[49],"question:":[50],"given":[51],"an":[52,55],"encoding":[53],"image,":[56,137],"which":[58],"extent":[59],"is":[60],"it":[61],"possible":[62],"reconstruct":[64],"itself?":[67],"To":[68],"answer":[69],"question":[71],"contribute":[73],"general":[75],"framework":[76],"invert":[78,86],"representations.":[79],"We":[80,102],"show":[81,125],"that":[82,126],"method":[84],"can":[85],"such":[88],"as":[89],"HOG":[90],"more":[91],"accurately":[92],"than":[93],"recent":[94,112],"alternatives":[95],"while":[96],"being":[97],"applicable":[98],"CNNs":[100,130],"too.":[101],"then":[103],"use":[104],"technique":[106],"study":[108],"inverse":[110],"state-of-the-art":[113],"CNN":[114],"for":[117],"first":[119],"time.":[120],"Among":[121],"findings,":[123],"several":[127],"layers":[128],"retain":[131],"photographically":[132],"accurate":[133],"about":[135],"with":[138],"different":[139],"degrees":[140],"geometric":[142],"photometric":[144],"invariance.":[145]},"counts_by_year":[{"year":2026,"cited_by_count":12},{"year":2025,"cited_by_count":84},{"year":2024,"cited_by_count":102},{"year":2023,"cited_by_count":177},{"year":2022,"cited_by_count":170},{"year":2021,"cited_by_count":260},{"year":2020,"cited_by_count":265},{"year":2019,"cited_by_count":271},{"year":2018,"cited_by_count":226},{"year":2017,"cited_by_count":139},{"year":2016,"cited_by_count":119},{"year":2015,"cited_by_count":33},{"year":2014,"cited_by_count":3},{"year":2012,"cited_by_count":1}],"updated_date":"2026-04-18T07:56:08.524223","created_date":"2025-10-10T00:00:00"}