{"id":"https://openalex.org/W2901197893","doi":null,"title":"Enhancing the Robustness of Prior Network in Out-of-Distribution Detection.","display_name":"Enhancing the Robustness of Prior Network in Out-of-Distribution Detection.","publication_year":2018,"publication_date":"2018-11-18","ids":{"openalex":"https://openalex.org/W2901197893","mag":"2901197893"},"language":"en","primary_location":{"id":"mag:2901197893","doi":null,"is_oa":false,"landing_page_url":"http://export.arxiv.org/pdf/1811.07308","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":null},"volume":null,"type":"article","indexed_in":[],"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/A5103103242","display_name":"Wenhu Chen","orcid":"https://orcid.org/0000-0002-8609-7821","observed_orcids":["https://orcid.org/0000-0002-8609-7821"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wenhu Chen","raw_affiliation_strings":[],"raw_orcid":"https://orcid.org/0000-0002-8609-7821","affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5058974318","display_name":"Yilin Shen","orcid":"https://orcid.org/0000-0002-1955-1529","observed_orcids":["https://orcid.org/0000-0002-1955-1529","https://orcid.org/0009-0008-5832-520X"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yilin Shen","raw_affiliation_strings":[],"raw_orcid":"https://orcid.org/0000-0002-1955-1529","affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5080890357","display_name":"Xin Wang","orcid":"https://orcid.org/0000-0001-9394-3865","observed_orcids":["https://orcid.org/0000-0001-9394-3865","https://orcid.org/0009-0008-4483-4310"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xin Wang","raw_affiliation_strings":[],"raw_orcid":"https://orcid.org/0000-0001-9394-3865","affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5100702485","display_name":"William Yang Wang","orcid":"https://orcid.org/0000-0001-6153-8240","observed_orcids":["https://orcid.org/0000-0001-6153-8240","https://orcid.org/0000-0001-9669-4841"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"William Yang Wang","raw_affiliation_strings":[],"raw_orcid":"https://orcid.org/0000-0001-6153-8240","affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.8487,"has_fulltext":false,"cited_by_count":7,"citation_normalized_percentile":{"value":0.78620737,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":88,"max":97},"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.986299991607666,"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":0.986299991607666,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.00559999980032444,"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.00139999995008111,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/out-of-distribution-detection","display_name":"out-of-distribution detection","score":1.0},{"id":"https://openalex.org/keywords/entropy-based-uncertainty","display_name":"entropy-based uncertainty","score":0.7889999747276306},{"id":"https://openalex.org/keywords/robustness","display_name":"robustness","score":0.7269999980926514},{"id":"https://openalex.org/keywords/pre-trained-deep-neural-networks","display_name":"pre-trained deep neural networks","score":0.6970000267028809}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7334415912628174},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.713694155216217},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6886193156242371},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.606131911277771},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.593822717666626},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5325514078140259},{"id":"https://openalex.org/C106301342","wikidata":"https://www.wikidata.org/wiki/Q4117933","display_name":"Entropy (arrow of time)","level":2,"score":0.5189125537872314},{"id":"https://openalex.org/C123657996","wikidata":"https://www.wikidata.org/wiki/Q12271","display_name":"Architecture","level":2,"score":0.4851301610469818},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.48484671115875244},{"id":"https://openalex.org/C167981619","wikidata":"https://www.wikidata.org/wiki/Q1685498","display_name":"Cross entropy","level":3,"score":0.44100427627563477},{"id":"https://openalex.org/C9679016","wikidata":"https://www.wikidata.org/wiki/Q1417473","display_name":"Principle of maximum entropy","level":2,"score":0.3918256163597107},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3721160888671875},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3480173349380493},{"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/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0},{"id":"https://openalex.org/C153349607","wikidata":"https://www.wikidata.org/wiki/Q36649","display_name":"Visual arts","level":1,"score":0.0},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"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/C142362112","wikidata":"https://www.wikidata.org/wiki/Q735","display_name":"Art","level":0,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"mag:2901197893","doi":null,"is_oa":false,"landing_page_url":"http://export.arxiv.org/pdf/1811.07308","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":null}],"best_oa_location":null,"sustainable_development_goals":[],"sustainable_development_goals_aurora":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":16,"referenced_works":["https://openalex.org/W967544008","https://openalex.org/W1614298861","https://openalex.org/W1686810756","https://openalex.org/W2017814585","https://openalex.org/W2108598243","https://openalex.org/W2132339004","https://openalex.org/W2194775991","https://openalex.org/W2243397390","https://openalex.org/W2335728318","https://openalex.org/W2525778437","https://openalex.org/W2549139847","https://openalex.org/W2626778328","https://openalex.org/W2899771611","https://openalex.org/W2963558691","https://openalex.org/W2964137095","https://openalex.org/W3118608800"],"related_works":["https://openalex.org/W3096315052","https://openalex.org/W2973900266","https://openalex.org/W3012922053","https://openalex.org/W3034745658","https://openalex.org/W2949598554","https://openalex.org/W2892430965","https://openalex.org/W3203111377","https://openalex.org/W3093984614","https://openalex.org/W2949441870","https://openalex.org/W3206281198","https://openalex.org/W3089919818","https://openalex.org/W2554000980","https://openalex.org/W3195497129","https://openalex.org/W3137479890","https://openalex.org/W3090897510","https://openalex.org/W2953054173","https://openalex.org/W3175562011","https://openalex.org/W2761709036","https://openalex.org/W3175032500","https://openalex.org/W3011870832"],"abstract_inverted_index":{"With":[0],"the":[1,87,107,120],"recent":[2],"surge":[3],"of":[4,89],"interests":[5],"in":[6,16],"deep":[7,19,122],"neural":[8,20],"networks,":[9],"more":[10,142],"real-world\napplications":[11],"start":[12],"to":[13,23,46],"adopt":[14],"it":[15],"practice.":[17],"However,":[18],"networks":[21],"are\nknown":[22],"have":[24],"limited":[25],"control":[26],"over":[27],"its":[28,153],"prediction":[29],"under":[30],"unseen":[31],"images.":[32,64],"Such\nweakness":[33],"can":[34,75,116],"potentially":[35],"threaten":[36],"society":[37],"and":[38,109,127,135],"cause":[39],"annoying":[40],"consequences":[41],"in\nreal-world":[42],"scenarios.":[43],"In":[44,65],"order":[45],"resolve":[47],"such":[48],"issue,":[49],"a":[50,69,95],"popular":[51],"task":[52],"called\nout-of-distribution":[53],"detection":[54],"was":[55],"proposed,":[56],"which":[57,74,99],"aims":[58],"at":[59],"separating\nout-of-distribution":[60],"images":[61,111],"from":[62,79],"in-distribution":[63],"this":[66],"paper,":[67],"we\npropose":[68],"perturbed":[70],"prior":[71,83],"network":[72],"architecture,":[73],"efficiently":[76],"separate\nmodel-level":[77],"uncertainty":[78,81,92],"data-level":[80],"via":[82],"entropy.":[84],"To\nfurther":[85],"enhance":[86],"robustness":[88],"proposed":[90],"entropy-based":[91],"measure,\nwe":[93],"propose":[94],"concentration":[96,103],"perturbation":[97],"algorithm,":[98],"adaptively":[100],"adds":[101],"noise\nto":[102],"parameters":[104],"so":[105],"that":[106],"in-":[108],"out-of-distribution":[110,136],"are\nbetter":[112],"separable.":[113],"Our":[114],"method":[115,141],"directly":[117],"rely":[118],"on":[119,158],"pre-trained":[121],"neural\nnetwork":[123],"without":[124],"re-training":[125],"it,":[126],"also":[128],"requires":[129],"no":[130],"knowledge":[131],"about":[132],"the\nnetwork":[133],"architecture":[134],"examples.":[137],"Such":[138],"simplicity":[139],"makes\nour":[140],"suitable":[143],"for":[144],"real-world":[145],"AI":[146],"applications.":[147],"Through":[148],"comprehensive\nexperiments,":[149],"our":[150],"methods":[151],"demonstrate":[152],"superiority":[154],"by":[155],"achieving\nstate-of-the-art":[156],"results":[157],"many":[159],"datasets.":[160]},"counts_by_year":[{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":3},{"year":2020,"cited_by_count":3}],"updated_date":"2026-10-12T09:40:04.860316","created_date":"2025-10-10T00:00:00"}
