{"id":"https://openalex.org/W2980611702","doi":"https://doi.org/10.1109/icip.2019.8804396","title":"Reve: Regularizing Deep Learning with Variational Entropy Bound","display_name":"Reve: Regularizing Deep Learning with Variational Entropy Bound","publication_year":2019,"publication_date":"2019-08-26","ids":{"openalex":"https://openalex.org/W2980611702","doi":"https://doi.org/10.1109/icip.2019.8804396","mag":"2980611702"},"language":"en","primary_location":{"id":"doi:10.1109/icip.2019.8804396","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icip.2019.8804396","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE International Conference on Image Processing (ICIP)","raw_type":"proceedings-article"},"type":"preprint","indexed_in":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/1910.06816","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5063633079","display_name":"Antoine Saporta","orcid":null},"institutions":[{"id":"https://openalex.org/I39804081","display_name":"Sorbonne Universit\u00e9","ror":"https://ror.org/02en5vm52","country_code":"FR","type":"education","lineage":["https://openalex.org/I39804081"]}],"countries":["FR"],"is_corresponding":true,"raw_author_name":"Antoine Saporta","raw_affiliation_strings":["Sorbonne Universit\u00e9, Paris, France"],"affiliations":[{"raw_affiliation_string":"Sorbonne Universit\u00e9, Paris, France","institution_ids":["https://openalex.org/I39804081"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100767071","display_name":"Yifu Chen","orcid":"https://orcid.org/0009-0008-0925-3115"},"institutions":[{"id":"https://openalex.org/I39804081","display_name":"Sorbonne Universit\u00e9","ror":"https://ror.org/02en5vm52","country_code":"FR","type":"education","lineage":["https://openalex.org/I39804081"]}],"countries":["FR"],"is_corresponding":false,"raw_author_name":"Yifu Chen","raw_affiliation_strings":["Sorbonne Universit\u00e9, Paris, France"],"affiliations":[{"raw_affiliation_string":"Sorbonne Universit\u00e9, Paris, France","institution_ids":["https://openalex.org/I39804081"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5112030698","display_name":"Micha\u00ebl Blot","orcid":null},"institutions":[{"id":"https://openalex.org/I39804081","display_name":"Sorbonne Universit\u00e9","ror":"https://ror.org/02en5vm52","country_code":"FR","type":"education","lineage":["https://openalex.org/I39804081"]}],"countries":["FR"],"is_corresponding":false,"raw_author_name":"Michael Blot","raw_affiliation_strings":["Sorbonne Universit\u00e9, Paris, France"],"affiliations":[{"raw_affiliation_string":"Sorbonne Universit\u00e9, Paris, France","institution_ids":["https://openalex.org/I39804081"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5108118084","display_name":"Matthieu Cord","orcid":"https://orcid.org/0000-0002-0627-5844"},"institutions":[{"id":"https://openalex.org/I39804081","display_name":"Sorbonne Universit\u00e9","ror":"https://ror.org/02en5vm52","country_code":"FR","type":"education","lineage":["https://openalex.org/I39804081"]}],"countries":["FR"],"is_corresponding":false,"raw_author_name":"Matthieu Cord","raw_affiliation_strings":["Sorbonne Universit\u00e9, Paris, France"],"affiliations":[{"raw_affiliation_string":"Sorbonne Universit\u00e9, Paris, France","institution_ids":["https://openalex.org/I39804081"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5063633079"],"corresponding_institution_ids":["https://openalex.org/I39804081"],"apc_list":null,"apc_paid":null,"fwci":0.1935,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.52320337,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":93},"biblio":{"volume":null,"issue":null,"first_page":"1610","last_page":"1614"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10500","display_name":"Sparse and Compressive Sensing Techniques","score":0.9997000098228455,"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":0.9997000098228455,"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/T12814","display_name":"Gaussian Processes and Bayesian Inference","score":0.9990000128746033,"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":0.998199999332428,"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/regularization","display_name":"Regularization (linguistics)","score":0.6317577958106995},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6256284117698669},{"id":"https://openalex.org/keywords/entropy","display_name":"Entropy (arrow of time)","score":0.5886646509170532},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5599203705787659},{"id":"https://openalex.org/keywords/conditional-entropy","display_name":"Conditional entropy","score":0.5450620055198669},{"id":"https://openalex.org/keywords/upper-and-lower-bounds","display_name":"Upper and lower bounds","score":0.5136160254478455},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.48103171586990356},{"id":"https://openalex.org/keywords/generalization","display_name":"Generalization","score":0.4775282144546509},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.42661619186401367},{"id":"https://openalex.org/keywords/mathematical-optimization","display_name":"Mathematical optimization","score":0.34263405203819275},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.3192020058631897},{"id":"https://openalex.org/keywords/principle-of-maximum-entropy","display_name":"Principle of maximum entropy","score":0.23940491676330566}],"concepts":[{"id":"https://openalex.org/C2776135515","wikidata":"https://www.wikidata.org/wiki/Q17143721","display_name":"Regularization (linguistics)","level":2,"score":0.6317577958106995},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6256284117698669},{"id":"https://openalex.org/C106301342","wikidata":"https://www.wikidata.org/wiki/Q4117933","display_name":"Entropy (arrow of time)","level":2,"score":0.5886646509170532},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5599203705787659},{"id":"https://openalex.org/C101721835","wikidata":"https://www.wikidata.org/wiki/Q813908","display_name":"Conditional entropy","level":3,"score":0.5450620055198669},{"id":"https://openalex.org/C77553402","wikidata":"https://www.wikidata.org/wiki/Q13222579","display_name":"Upper and lower bounds","level":2,"score":0.5136160254478455},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.48103171586990356},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.4775282144546509},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.42661619186401367},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.34263405203819275},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3192020058631897},{"id":"https://openalex.org/C9679016","wikidata":"https://www.wikidata.org/wiki/Q1417473","display_name":"Principle of maximum entropy","level":2,"score":0.23940491676330566},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","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/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1109/icip.2019.8804396","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icip.2019.8804396","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE International Conference on Image Processing (ICIP)","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:1910.06816","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1910.06816","pdf_url":"https://arxiv.org/pdf/1910.06816","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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"pmh:oai:HAL:hal-02316946v1","is_oa":false,"landing_page_url":"https://hal.science/hal-02316946","pdf_url":null,"source":{"id":"https://openalex.org/S4306402512","display_name":"HAL (Le Centre pour la Communication Scientifique Directe)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1294671590","host_organization_name":"Centre National de la Recherche Scientifique","host_organization_lineage":["https://openalex.org/I1294671590"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"2019 IEEE International Conference on Image Processing (ICIP), Sep 2019, Taipei, Taiwan. pp.1610-1614, &#x27E8;10.1109/ICIP.2019.8804396&#x27E9;","raw_type":"Conference papers"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:1910.06816","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1910.06816","pdf_url":"https://arxiv.org/pdf/1910.06816","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":"submittedVersion","is_accepted":false,"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":41,"referenced_works":["https://openalex.org/W582134693","https://openalex.org/W1551759367","https://openalex.org/W1686946872","https://openalex.org/W1836465849","https://openalex.org/W2095705004","https://openalex.org/W2097117768","https://openalex.org/W2144513243","https://openalex.org/W2163605009","https://openalex.org/W2194775991","https://openalex.org/W2335728318","https://openalex.org/W2617539226","https://openalex.org/W2683470288","https://openalex.org/W2785468280","https://openalex.org/W2902227449","https://openalex.org/W2949117887","https://openalex.org/W2953384591","https://openalex.org/W2963862692","https://openalex.org/W2964059111","https://openalex.org/W2964080999","https://openalex.org/W2964137095","https://openalex.org/W2964160479","https://openalex.org/W2979454998","https://openalex.org/W3118608800","https://openalex.org/W3137695714","https://openalex.org/W4229494842","https://openalex.org/W4293469690","https://openalex.org/W4297347392","https://openalex.org/W6617145748","https://openalex.org/W6637108112","https://openalex.org/W6638667902","https://openalex.org/W6674330103","https://openalex.org/W6674914833","https://openalex.org/W6681151457","https://openalex.org/W6713132643","https://openalex.org/W6713134421","https://openalex.org/W6729906282","https://openalex.org/W6740376374","https://openalex.org/W6748498378","https://openalex.org/W6752757957","https://openalex.org/W6763485134","https://openalex.org/W6787972765"],"related_works":["https://openalex.org/W3005577593","https://openalex.org/W1550757207","https://openalex.org/W2003770568","https://openalex.org/W2186435021","https://openalex.org/W2161963661","https://openalex.org/W2350489049","https://openalex.org/W2167949116","https://openalex.org/W4229448040","https://openalex.org/W1795191888","https://openalex.org/W2982586717"],"abstract_inverted_index":{"Studies":[0],"on":[1,81,111],"generalization":[2],"performance":[3],"of":[4,11,70,103],"machine":[5],"learning":[6],"algorithms":[7],"under":[8],"the":[9,40,58,66,100,104],"scope":[10],"information":[12],"theory":[13],"suggest":[14],"that":[15,38,53,96],"compressed":[16],"representations":[17],"can":[18,42],"guarantee":[19],"good":[20],"generalization,":[21],"inspiring":[22],"many":[23],"compression-based":[24],"regularization":[25,35],"methods.":[26],"In":[27],"this":[28,71,82],"paper,":[29],"we":[30,75,87],"introduce":[31,76],"REVE,":[32],"a":[33,51,77,89,93],"new":[34],"scheme.":[36],"Noting":[37],"compressing":[39,65],"representation":[41],"be":[43],"sub-optimal,":[44],"our":[45],"first":[46],"contribution":[47],"is":[48,54,97],"to":[49,91],"identify":[50],"variable":[52],"directly":[55],"responsible":[56],"for":[57],"final":[59],"prediction.":[60],"Our":[61],"method":[62],"aims":[63],"at":[64],"class":[67],"conditioned":[68],"entropy":[69,84],"latter":[72],"variable.":[73],"Second,":[74],"variational":[78],"upper":[79],"bound":[80],"conditional":[83],"term.":[85],"Finally,":[86],"propose":[88],"scheme":[90],"instantiate":[92],"tractable":[94],"loss":[95],"integrated":[98],"within":[99],"training":[101],"procedure":[102],"neural":[105,113],"network":[106],"and":[107,115],"demonstrate":[108],"its":[109],"efficiency":[110],"different":[112],"networks":[114],"datasets.":[116]},"counts_by_year":[{"year":2021,"cited_by_count":1}],"updated_date":"2026-03-26T15:22:09.906841","created_date":"2025-10-10T00:00:00"}
