Adaptive Learning for Dynamic Features and Noisy Labels
Applying current machine learning algorithms in complex and open environments remains challenging, especially when different changing elements are coupled and the training data is scarce. For example, in the activity recognition task, the motion sensors may change position or fall off due to the int...
Uložené v:
| Vydané v: | IEEE transactions on pattern analysis and machine intelligence Ročník 47; číslo 2; s. 1219 - 1237 |
|---|---|
| Hlavní autori: | , , , |
| Médium: | Journal Article |
| Jazyk: | English |
| Vydavateľské údaje: |
United States
IEEE
01.02.2025
|
| Predmet: | |
| ISSN: | 0162-8828, 1939-3539, 2160-9292, 1939-3539 |
| On-line prístup: | Získať plný text |
| Tagy: |
Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
|
| Abstract | Applying current machine learning algorithms in complex and open environments remains challenging, especially when different changing elements are coupled and the training data is scarce. For example, in the activity recognition task, the motion sensors may change position or fall off due to the intensity of the activity, leading to changes in feature space and finally resulting in label noise. Learning from such a problem where the dynamic features are coupled with noisy labels is crucial but rarely studied, particularly when the noisy samples in new feature space are limited. In this paper, we tackle the above problem by proposing a novel two-stage algorithm, called Adaptive Learning for Dynamic features and Noisy labels (ALDN). Specifically, optimal transport is first modified to map the previously learned heterogeneous model to the prior model of the current stage. Then, to fully reuse the mapped prior model, we add a simple yet efficient regularizer as the consistency constraint to assist both the estimation of the noise transition matrix and the model training in the current stage. Finally, two implementations with direct (ALDN-D) and indirect (ALDN-ID) constraints are illustrated for better investigation. More importantly, we provide theoretical guarantees for risk minimization of ALDN-D and ALDN-ID. Extensive experiments validate the effectiveness of the proposed algorithms. |
|---|---|
| AbstractList | Applying current machine learning algorithms in complex and open environments remains challenging, especially when different changing elements are coupled and the training data is scarce. For example, in the activity recognition task, the motion sensors may change position or fall off due to the intensity of the activity, leading to changes in feature space and finally resulting in label noise. Learning from such a problem where the dynamic features are coupled with noisy labels is crucial but rarely studied, particularly when the noisy samples in new feature space are limited. In this paper, we tackle the above problem by proposing a novel two-stage algorithm, called Adaptive Learning for Dynamic features and Noisy labels (ALDN). Specifically, optimal transport is first modified to map the previously learned heterogeneous model to the prior model of the current stage. Then, to fully reuse the mapped prior model, we add a simple yet efficient regularizer as the consistency constraint to assist both the estimation of the noise transition matrix and the model training in the current stage. Finally, two implementations with direct (ALDN-D) and indirect (ALDN-ID) constraints are illustrated for better investigation. More importantly, we provide theoretical guarantees for risk minimization of ALDN-D and ALDN-ID. Extensive experiments validate the effectiveness of the proposed algorithms. Applying current machine learning algorithms in complex and open environments remains challenging, especially when different changing elements are coupled and the training data is scarce. For example, in the activity recognition task, the motion sensors may change position or fall off due to the intensity of the activity, leading to changes in feature space and finally resulting in label noise. Learning from such a problem where the dynamic features are coupled with noisy labels is crucial but rarely studied, particularly when the noisy samples in new feature space are limited. In this paper, we tackle the above problem by proposing a novel two-stage algorithm, called Adaptive Learning for Dynamic features and Noisy labels (ALDN). Specifically, optimal transport is first modified to map the previously learned heterogeneous model to the prior model of the current stage. Then, to fully reuse the mapped prior model, we add a simple yet efficient regularizer as the consistency constraint to assist both the estimation of the noise transition matrix and the model training in the current stage. Finally, two implementations with direct (ALDN-D) and indirect (ALDN-ID) constraints are illustrated for better investigation. More importantly, we provide theoretical guarantees for risk minimization of ALDN-D and ALDN-ID. Extensive experiments validate the effectiveness of the proposed algorithms.Applying current machine learning algorithms in complex and open environments remains challenging, especially when different changing elements are coupled and the training data is scarce. For example, in the activity recognition task, the motion sensors may change position or fall off due to the intensity of the activity, leading to changes in feature space and finally resulting in label noise. Learning from such a problem where the dynamic features are coupled with noisy labels is crucial but rarely studied, particularly when the noisy samples in new feature space are limited. In this paper, we tackle the above problem by proposing a novel two-stage algorithm, called Adaptive Learning for Dynamic features and Noisy labels (ALDN). Specifically, optimal transport is first modified to map the previously learned heterogeneous model to the prior model of the current stage. Then, to fully reuse the mapped prior model, we add a simple yet efficient regularizer as the consistency constraint to assist both the estimation of the noise transition matrix and the model training in the current stage. Finally, two implementations with direct (ALDN-D) and indirect (ALDN-ID) constraints are illustrated for better investigation. More importantly, we provide theoretical guarantees for risk minimization of ALDN-D and ALDN-ID. Extensive experiments validate the effectiveness of the proposed algorithms. |
| Author | Gu, Shilin Hou, Chenping Xu, Chao Hu, Dewen |
| Author_xml | – sequence: 1 givenname: Shilin orcidid: 0000-0003-1681-5856 surname: Gu fullname: Gu, Shilin email: gslnudt@outlook.com organization: College of Science, National University of Defense Technology, Changsha, China – sequence: 2 givenname: Chao orcidid: 0009-0009-4322-0699 surname: Xu fullname: Xu, Chao email: xcnudt@hotmail.com organization: College of Science, National University of Defense Technology, Changsha, China – sequence: 3 givenname: Dewen orcidid: 0000-0001-7357-0053 surname: Hu fullname: Hu, Dewen email: dwhu@nudt.edu.cn organization: College of Intelligence Science and Technology, National University of Defense Technology, Changsha, China – sequence: 4 givenname: Chenping orcidid: 0000-0002-9335-0469 surname: Hou fullname: Hou, Chenping email: houchenping@nudt.edu.cn organization: College of Science, National University of Defense Technology, Changsha, China |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/39480720$$D View this record in MEDLINE/PubMed |
| BookMark | eNp9kMtKw0AUhgep2Iu-gIhk6SZ1rpnJslSrhXhZ1HWYyZzISJrUmUTo25vaKuJCOHA233cO_z9Gg7qpAaFzgqeE4PR69Tx7WE4ppnzKuEopkUdoREmC45SmdIBGmCQ0VoqqIRqH8IYx4QKzEzRkKVdYUjxCcmb1pnUfEGWgfe3q16hsfHSzrfXaFdECdNt5CJGubfTYuLCNMm2gCqfouNRVgLPDnqCXxe1qfh9nT3fL-SyLC0Z5GwtagFUisUzJ0phSCMGNtSXBRpt-lBY4sSplTCapUVISaZjlRpYJFJwSNkFX-7sb37x3ENp87UIBVaVraLqQM0JZn4SRtEcvD2hn1mDzjXdr7bf5d9geoHug8E0IHsofhOB812j-1Wi-azQ_NNpL6o9UuFa3rqlbr131v3qxVx0A_PolORZCsU-fNoI_ |
| CODEN | ITPIDJ |
| CitedBy_id | crossref_primary_10_1016_j_patcog_2025_111719 |
| Cites_doi | 10.1109/TPAMI.2017.2769047 10.1109/CVPR.2017.240 10.1109/TSP.2012.2218810 10.1609/aaai.v33i01.33015256 10.1109/TPAMI.2015.2456899 10.1016/j.patcog.2021.108362 10.1109/ICCV.2015.168 10.1007/978-3-319-46379-7_1 10.1109/TPAMI.2020.2994749 10.1007/s10479-005-5724-z 10.1007/s11704-016-6906-3 10.1109/TKDE.2016.2563424 10.1007/978-3-031-19803-8_1 10.1007/978-3-540-71050-9 10.1609/aaai.v33i01.33013232 10.1109/TKDE.2021.3061215 10.1109/TNNLS.2020.2981386 10.1609/aaai.v35i9.16944 10.1109/TNNLS.2022.3152527 10.1109/CVPR52688.2022.01613 10.1016/j.neunet.2017.10.007 10.3390/s140609995 10.1609/aaai.v35i5.16532 10.1613/jair.1.12125 10.1109/CVPR.2015.7298885 10.1007/s11704-016-5489-3 10.1109/BigData47090.2019.9006373 10.1109/TNNLS.2013.2292894 10.1609/aaai.v31i1.10894 10.14778/3157794.3157797 10.1145/3534678.3539351 10.1109/TNNLS.2022.3178880 10.1007/978-3-030-01249-6_38 |
| ContentType | Journal Article |
| DBID | 97E RIA RIE AAYXX CITATION NPM 7X8 |
| DOI | 10.1109/TPAMI.2024.3489217 |
| DatabaseName | IEEE Xplore (IEEE) IEEE All-Society Periodicals Package (ASPP) 1998–Present IEEE Electronic Library (IEL) CrossRef PubMed MEDLINE - Academic |
| DatabaseTitle | CrossRef PubMed MEDLINE - Academic |
| DatabaseTitleList | PubMed MEDLINE - Academic |
| Database_xml | – sequence: 1 dbid: NPM name: PubMed url: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database – sequence: 2 dbid: RIE name: IEEE Electronic Library (IEL) url: https://ieeexplore.ieee.org/ sourceTypes: Publisher – sequence: 3 dbid: 7X8 name: MEDLINE - Academic url: https://search.proquest.com/medline sourceTypes: Aggregation Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Engineering Computer Science |
| EISSN | 2160-9292 1939-3539 |
| EndPage | 1237 |
| ExternalDocumentID | 39480720 10_1109_TPAMI_2024_3489217 10740558 |
| Genre | orig-research Journal Article |
| GrantInformation_xml | – fundername: Key NSF of China grantid: 62136005; 62036013 – fundername: National Science Fund for Distinguished Young Scholars grantid: 62425607 funderid: 10.13039/501100014219 |
| GroupedDBID | --- -DZ -~X .DC 0R~ 29I 4.4 53G 5GY 5VS 6IK 97E 9M8 AAJGR AARMG AASAJ AAWTH ABAZT ABFSI ABQJQ ABVLG ACGFO ACGFS ACIWK ACNCT ADRHT AENEX AETEA AETIX AGQYO AGSQL AHBIQ AI. AIBXA AKJIK AKQYR ALLEH ALMA_UNASSIGNED_HOLDINGS ASUFR ATWAV BEFXN BFFAM BGNUA BKEBE BPEOZ CS3 DU5 E.L EBS EJD F5P FA8 HZ~ H~9 IBMZZ ICLAB IEDLZ IFIPE IFJZH IPLJI JAVBF LAI M43 MS~ O9- OCL P2P PQQKQ RIA RIE RNI RNS RXW RZB TAE TN5 UHB VH1 XJT ~02 AAYXX CITATION AAYOK NPM RIG 7X8 |
| ID | FETCH-LOGICAL-c324t-52ced856d387fbbf5554bddf10babbab8a506d8933769b87717b3d4b7f6ec4213 |
| IEDL.DBID | RIE |
| ISICitedReferencesCount | 1 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=001395340500023&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 0162-8828 1939-3539 |
| IngestDate | Sun Sep 28 10:47:31 EDT 2025 Wed Mar 05 02:44:41 EST 2025 Sat Nov 29 02:58:28 EST 2025 Tue Nov 18 22:27:46 EST 2025 Wed Aug 27 01:58:00 EDT 2025 |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 2 |
| Language | English |
| License | https://ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html https://doi.org/10.15223/policy-029 https://doi.org/10.15223/policy-037 |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c324t-52ced856d387fbbf5554bddf10babbab8a506d8933769b87717b3d4b7f6ec4213 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 |
| ORCID | 0000-0002-9335-0469 0000-0003-1681-5856 0000-0001-7357-0053 0009-0009-4322-0699 |
| PMID | 39480720 |
| PQID | 3123072319 |
| PQPubID | 23479 |
| PageCount | 19 |
| ParticipantIDs | ieee_primary_10740558 proquest_miscellaneous_3123072319 crossref_primary_10_1109_TPAMI_2024_3489217 pubmed_primary_39480720 crossref_citationtrail_10_1109_TPAMI_2024_3489217 |
| PublicationCentury | 2000 |
| PublicationDate | 2025-02-01 |
| PublicationDateYYYYMMDD | 2025-02-01 |
| PublicationDate_xml | – month: 02 year: 2025 text: 2025-02-01 day: 01 |
| PublicationDecade | 2020 |
| PublicationPlace | United States |
| PublicationPlace_xml | – name: United States |
| PublicationTitle | IEEE transactions on pattern analysis and machine intelligence |
| PublicationTitleAbbrev | TPAMI |
| PublicationTitleAlternate | IEEE Trans Pattern Anal Mach Intell |
| PublicationYear | 2025 |
| Publisher | IEEE |
| Publisher_xml | – name: IEEE |
| References | ref13 ref56 Yao (ref11) ref15 Liu (ref44) ref55 Han (ref10) ref54 Zhang (ref43) ref17 ref16 ref19 ref18 Hou (ref7) Mohri (ref50) 2012 ref51 Cheng (ref34) Li (ref24) Cheng (ref38) ref46 Cha (ref52) 2007; 1 ref48 ref47 ref42 Yao (ref26) ref49 Goldberger (ref33) ref8 Perkins (ref14) Vahdat (ref25) ref9 ref4 ref3 ref6 Goodfellow (ref41) ref40 Bartlett (ref53) 2002; 3 Natarajan (ref12) ref35 ref37 ref31 ref32 ref2 ref1 ref39 Crammer (ref21) 2006; 7 Cheng (ref36) Kremer (ref23) Patrini (ref30) ref20 Zhang (ref28) ref22 ref27 ref29 Han (ref5) 2020 Villani (ref45) 2009; 338 |
| References_xml | – ident: ref3 doi: 10.1109/TPAMI.2017.2769047 – start-page: 5596 volume-title: Proc. Adv. Neural Inf. Process. Syst. 30: Annu. Conf. Neural Inf. Process. Syst. ident: ref25 article-title: Toward robustness against label noise in training deep discriminative neural networks – start-page: 1789 volume-title: Proc. 37th Int. Conf. Mach. Learn. ident: ref36 article-title: Learning with bounded instance and label-dependent label noise – ident: ref13 doi: 10.1109/CVPR.2017.240 – ident: ref46 doi: 10.1109/TSP.2012.2218810 – start-page: 1196 volume-title: Proc. Adv. Neural Inf. Process. Syst. 26: 27th Annu. Conf. Neural Inf. Process. Syst. ident: ref12 article-title: Learning with noisy labels – ident: ref51 doi: 10.1609/aaai.v33i01.33015256 – start-page: 308 volume-title: Proc. Int. Conf. Artif. Intell. Statist. ident: ref23 article-title: Robust active label correction – ident: ref27 doi: 10.1109/TPAMI.2015.2456899 – volume-title: Proc. 9th Int. Conf. Learn. Representations ident: ref34 article-title: Learning with instance-dependent label noise: A sample sieve approach – ident: ref55 doi: 10.1016/j.patcog.2021.108362 – ident: ref39 doi: 10.1109/ICCV.2015.168 – ident: ref49 doi: 10.1007/978-3-319-46379-7_1 – ident: ref9 doi: 10.1109/TPAMI.2020.2994749 – start-page: 4006 volume-title: Proc. 37th Int. Conf. Mach. Learn. ident: ref10 article-title: SIGUA: Forgetting may make learning with noisy labels more robust – ident: ref47 doi: 10.1007/s10479-005-5724-z – volume-title: Proc. Adv. Neural Inf. Process. Syst. 35: Annu. Conf. Neural Inf. Process. Syst. ident: ref38 article-title: Class-dependent label-noise learning with cycle-consistency regularization – ident: ref1 doi: 10.1007/s11704-016-6906-3 – ident: ref6 doi: 10.1109/TKDE.2016.2563424 – start-page: 1417 volume-title: Proc. Adv. Neural Inf. Process. Syst. ident: ref7 article-title: Learning with feature evolvable streams – start-page: 8792 volume-title: Proc. Adv. Neural Inf. Process. Syst. 31: Annu. Conf. Neural Inf. Process. Syst. ident: ref43 article-title: Generalized cross entropy loss for training deep neural networks with noisy labels – ident: ref22 doi: 10.1007/978-3-031-19803-8_1 – start-page: 708 volume-title: Proc. 33nd Int. Conf. Mach. Learn. ident: ref30 article-title: Loss factorization, weakly supervised learning and label noise robustness – volume: 338 volume-title: Optimal Transport: Old and New year: 2009 ident: ref45 doi: 10.1007/978-3-540-71050-9 – ident: ref17 doi: 10.1609/aaai.v33i01.33013232 – start-page: 6226 volume-title: Proc. 37th Int. Conf. Mach. Learn. ident: ref44 article-title: Peer loss functions: Learning from noisy labels without knowing noise rates – start-page: 592 volume-title: Proc. 20th Int. Conf. Mach. Learn. ident: ref14 article-title: Online feature selection using grafting – ident: ref31 doi: 10.1109/TKDE.2021.3061215 – start-page: 4313 volume-title: Proc. 23rd Int. Conf. Artif. Intell. Statist. ident: ref24 article-title: Gradient descent with early stopping is provably robust to label noise for overparameterized neural networks – year: 2020 ident: ref5 article-title: A survey of label-noise representation learning: Past, present and future – ident: ref18 doi: 10.1109/TNNLS.2020.2981386 – volume-title: Proc. 5th Int. Conf. Learn. Representations ident: ref33 article-title: Training deep neural-networks using a noise adaptation layer – ident: ref16 doi: 10.1609/aaai.v35i9.16944 – volume-title: Proc. 3rd Int. Conf. Learn. Representations ident: ref41 article-title: Explaining and harnessing adversarial examples – volume: 7 start-page: 551 year: 2006 ident: ref21 article-title: Online passive-aggressive algorithms publication-title: J. Mach. Learn. Res. – volume-title: Foundations of Machine Learning year: 2012 ident: ref50 – volume: 1 start-page: 300 issue: 4 year: 2007 ident: ref52 article-title: Comprehensive survey on distance/similarity measures between probability density functions publication-title: Int. J. Math. Models Methods Appl. Sci. – ident: ref35 doi: 10.1109/TNNLS.2022.3152527 – ident: ref48 doi: 10.1109/CVPR52688.2022.01613 – ident: ref56 doi: 10.1016/j.neunet.2017.10.007 – ident: ref2 doi: 10.3390/s140609995 – ident: ref19 doi: 10.1609/aaai.v35i5.16532 – start-page: 10789 volume-title: Proc. 37th Int. Conf. Mach. Learn. ident: ref11 article-title: Searching to exploit memorization effect in learning with noisy labels – ident: ref54 doi: 10.1613/jair.1.12125 – ident: ref32 doi: 10.1109/CVPR.2015.7298885 – ident: ref15 doi: 10.1007/s11704-016-5489-3 – ident: ref8 doi: 10.1109/BigData47090.2019.9006373 – volume: 3 start-page: 463 year: 2002 ident: ref53 article-title: Rademacher and gaussian complexities: Risk bounds and structural results publication-title: J. Mach. Learn. Res. – ident: ref4 doi: 10.1109/TNNLS.2013.2292894 – start-page: 8792 volume-title: Proc. Adv. Neural Inf. Process. Syst. 31: Annu. Conf. Neural Inf. Process. Syst. ident: ref28 article-title: Generalized cross entropy loss for training deep neural networks with noisy labels – ident: ref42 doi: 10.1609/aaai.v31i1.10894 – ident: ref37 doi: 10.14778/3157794.3157797 – ident: ref29 doi: 10.1145/3534678.3539351 – ident: ref20 doi: 10.1109/TNNLS.2022.3178880 – start-page: 7260 volume-title: Proc. Adv. Neural Inf. Process. Syst. 33: Annu. Conf. Neural Inf. Process. Syst. ident: ref26 article-title: Dual T: Reducing estimation error for transition matrix in label-noise learning – ident: ref40 doi: 10.1007/978-3-030-01249-6_38 |
| SSID | ssj0014503 |
| Score | 2.4780343 |
| Snippet | Applying current machine learning algorithms in complex and open environments remains challenging, especially when different changing elements are coupled and... |
| SourceID | proquest pubmed crossref ieee |
| SourceType | Aggregation Database Index Database Enrichment Source Publisher |
| StartPage | 1219 |
| SubjectTerms | Adaptation models Adaptive learning Data models Dynamic feature space Feature extraction heterogeneous model reuse Heuristic algorithms incremental learning Noise Noise measurement noisy labels Risk minimization Streams Training |
| Title | Adaptive Learning for Dynamic Features and Noisy Labels |
| URI | https://ieeexplore.ieee.org/document/10740558 https://www.ncbi.nlm.nih.gov/pubmed/39480720 https://www.proquest.com/docview/3123072319 |
| Volume | 47 |
| WOSCitedRecordID | wos001395340500023&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVIEE databaseName: IEEE Electronic Library (IEL) customDbUrl: eissn: 2160-9292 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0014503 issn: 0162-8828 databaseCode: RIE dateStart: 19790101 isFulltext: true titleUrlDefault: https://ieeexplore.ieee.org/ providerName: IEEE |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1LS8QwEB5URPTg-7G-iOBNqu0maZLj4gMFXTwo7K3kVVmQrri7gv_eSdoue1EQeig0KWUeyTfNzHwA59x1c0u9TiijacIU9YmR2iWZyTU6n8_TuknSo-j35WCgnpti9VgL472PyWf-MtzGs3w3stPwq-wqJA-mnMtFWBQir4u1ZkcGjEcaZIQw6OIYR7QVMqm6ennuPT1gLNhll5RJhSh8FVaoCtXUged7bkOKDCu_g8246dxt_PNzN2G9QZekV5vDFiz4ahs2WuYG0jjyNqzNtSHcAdFz-iMse6TptvpGEMqSm5qsngSUOMWonOjKkf5oOP4mj9rgnroLr3e3L9f3SUOokFjETRMMOq13kueOSlEaU3LEEsa5MkuNNnhJzdPcIYLBVUcZKTDUM9QxI8rcW9bN6B4sVaPKHwBxSqDvlzl6MGNeccVL663QhmJQh886kLVSLWzTbTyQXrwXMepIVRGVUgSlFI1SOnAxm_NR99r4c_RuEPncyFraHThrtVegq4TzD1350XRc0CxkvSOgVR3Yr9U6m91aw-Evbz2C1W5g_o352sewNPmc-hNYtl-T4fjzFO1xIE-jPf4AK5PW-A |
| linkProvider | IEEE |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1JS8QwFH644XJwX8Y1gjeptk3SNsfBBcVx8DCCt5KtIkhnmEXw3_uStsNcFIQeCk1KyctLvq95730AF9zEiaZWBpTRMGCC2kBl0gSRSiQ6n03CqkhSJ-12s7c38VInq_tcGGutDz6zV-7Wn-Wbvp64X2XXLngw5Dybh0XOWBxW6VrTQwPGvRAyghh0cmQSTY5MKK57L-3nR2SDMbuiLBOIw1dhmQqXT-2Uvme2JK-x8jvc9NvO_cY_P3gT1mt8SdrVhNiCOVtuw0aj3UBqV96GtZlChDuQto0cuIWP1PVW3wmCWXJbydUThxMnyMuJLA3p9j9G36QjFe6qu_B6f9e7eQhqSYVAI3IaI-3U1mQ8MTRLC6UKjmhCGVNEoZIKr0zyMDGIYXDdESpLkewpaphKi8RqFkd0DxbKfmkPgBiRovcXCfowY1ZwwQttdSoVRVqHz1oQNaOa67reuJO9-Mw97whF7o2SO6PktVFacDntM6iqbfzZetcN-UzLarRbcN5YL0dncScgsrT9ySinkYt7R0grWrBfmXXau5kNh7-89QxWHnrPnbzz2H06gtXY6QD76O1jWBgPJ_YElvTX-GM0PPWz8gd1BtlX |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Adaptive+Learning+for+Dynamic+Features+and+Noisy+Labels&rft.jtitle=IEEE+transactions+on+pattern+analysis+and+machine+intelligence&rft.au=Gu%2C+Shilin&rft.au=Xu%2C+Chao&rft.au=Hu%2C+Dewen&rft.au=Hou%2C+Chenping&rft.date=2025-02-01&rft.pub=IEEE&rft.issn=0162-8828&rft.volume=47&rft.issue=2&rft.spage=1219&rft.epage=1237&rft_id=info:doi/10.1109%2FTPAMI.2024.3489217&rft_id=info%3Apmid%2F39480720&rft.externalDocID=10740558 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0162-8828&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0162-8828&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0162-8828&client=summon |