Self-supervised Learning: A Succinct Review
Machine learning has made significant advances in the field of image processing. The foundation of this success is supervised learning, which necessitates annotated labels generated by humans and hence learns from labelled data, whereas unsupervised learning learns from unlabeled data. Self-supervis...
Uloženo v:
| Vydáno v: | Archives of computational methods in engineering Ročník 30; číslo 4; s. 2761 - 2775 |
|---|---|
| Hlavní autoři: | , , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
Dordrecht
Springer Netherlands
01.05.2023
Springer Nature B.V |
| Témata: | |
| ISSN: | 1134-3060, 1886-1784, 1886-1784 |
| On-line přístup: | Získat plný text |
| Tagy: |
Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
|
| Abstract | Machine learning has made significant advances in the field of image processing. The foundation of this success is supervised learning, which necessitates annotated labels generated by humans and hence learns from labelled data, whereas unsupervised learning learns from unlabeled data. Self-supervised learning (SSL) is a type of un-supervised learning that helps in the performance of downstream computer vision tasks such as object detection, image comprehension, image segmentation, and so on. It can develop generic artificial intelligence systems at a low cost using unstructured and unlabeled data. The authors of this review article have presented detailed literature on self-supervised learning as well as its applications in different domains. The primary goal of this review article is to demonstrate how images learn from their visual features using self-supervised approaches. The authors have also discussed various terms used in self-supervised learning as well as different types of learning, such as contrastive learning, transfer learning, and so on. This review article describes in detail the pipeline of self-supervised learning, including its two main phases: pretext and downstream tasks. The authors have shed light on various challenges encountered while working on self-supervised learning at the end of the article. |
|---|---|
| AbstractList | Machine learning has made significant advances in the field of image processing. The foundation of this success is supervised learning, which necessitates annotated labels generated by humans and hence learns from labelled data, whereas unsupervised learning learns from unlabeled data. Self-supervised learning (SSL) is a type of un-supervised learning that helps in the performance of downstream computer vision tasks such as object detection, image comprehension, image segmentation, and so on. It can develop generic artificial intelligence systems at a low cost using unstructured and unlabeled data. The authors of this review article have presented detailed literature on self-supervised learning as well as its applications in different domains. The primary goal of this review article is to demonstrate how images learn from their visual features using self-supervised approaches. The authors have also discussed various terms used in self-supervised learning as well as different types of learning, such as contrastive learning, transfer learning, and so on. This review article describes in detail the pipeline of self-supervised learning, including its two main phases: pretext and downstream tasks. The authors have shed light on various challenges encountered while working on self-supervised learning at the end of the article. Machine learning has made significant advances in the field of image processing. The foundation of this success is supervised learning, which necessitates annotated labels generated by humans and hence learns from labelled data, whereas unsupervised learning learns from unlabeled data. Self-supervised learning (SSL) is a type of un-supervised learning that helps in the performance of downstream computer vision tasks such as object detection, image comprehension, image segmentation, and so on. It can develop generic artificial intelligence systems at a low cost using unstructured and unlabeled data. The authors of this review article have presented detailed literature on self-supervised learning as well as its applications in different domains. The primary goal of this review article is to demonstrate how images learn from their visual features using self-supervised approaches. The authors have also discussed various terms used in self-supervised learning as well as different types of learning, such as contrastive learning, transfer learning, and so on. This review article describes in detail the pipeline of self-supervised learning, including its two main phases: pretext and downstream tasks. The authors have shed light on various challenges encountered while working on self-supervised learning at the end of the article.Machine learning has made significant advances in the field of image processing. The foundation of this success is supervised learning, which necessitates annotated labels generated by humans and hence learns from labelled data, whereas unsupervised learning learns from unlabeled data. Self-supervised learning (SSL) is a type of un-supervised learning that helps in the performance of downstream computer vision tasks such as object detection, image comprehension, image segmentation, and so on. It can develop generic artificial intelligence systems at a low cost using unstructured and unlabeled data. The authors of this review article have presented detailed literature on self-supervised learning as well as its applications in different domains. The primary goal of this review article is to demonstrate how images learn from their visual features using self-supervised approaches. The authors have also discussed various terms used in self-supervised learning as well as different types of learning, such as contrastive learning, transfer learning, and so on. This review article describes in detail the pipeline of self-supervised learning, including its two main phases: pretext and downstream tasks. The authors have shed light on various challenges encountered while working on self-supervised learning at the end of the article. |
| Author | Kumar, Krishan Mittal, Ajay Kumar, Munish Nabi, Syed Tufael Rani, Veenu |
| Author_xml | – sequence: 1 givenname: Veenu surname: Rani fullname: Rani, Veenu organization: Department of Computational Sciences, Maharaja Ranjit Singh Punjab Technical University – sequence: 2 givenname: Syed Tufael surname: Nabi fullname: Nabi, Syed Tufael organization: Department of Computational Sciences, Maharaja Ranjit Singh Punjab Technical University – sequence: 3 givenname: Munish orcidid: 0000-0003-0115-1620 surname: Kumar fullname: Kumar, Munish email: munishcse@gmail.com organization: Department of Computational Sciences, Maharaja Ranjit Singh Punjab Technical University – sequence: 4 givenname: Ajay surname: Mittal fullname: Mittal, Ajay organization: University Institute of Engineering and Technology, Panjab University – sequence: 5 givenname: Krishan surname: Kumar fullname: Kumar, Krishan organization: University Institute of Engineering and Technology, Panjab University |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/36713767$$D View this record in MEDLINE/PubMed |
| BookMark | eNp9kVtLXDEUhUNR6qX-gT7IQF8Eic3tZCd9EAZRKwwItX0OMZdp5EzOmJwz0n_fM47W6oNPO7DX-lg7aw9t5S4HhD5TckIJga-VUsUpJoxjopUSmH1Au1QpiSkosTW-KReYE0l20F6td4Q0Qmv2Ee1wCZSDhF10fBPaiOuwDGWVavCTWbAlpzz_NplObgbnUnb95EdYpfDwCW1H29Zw8DT30a-L859n3_Hs-vLqbDrDToDosRVAPFGCOK2BAROsAYg6RtcQ6hpJgrBS-SitV-C8vQUuoxNM66i5957vo9MNdzncLoJ3IffFtmZZ0sKWP6azybze5PTbzLuV0aoBzdgIOHoClO5-CLU3i1RdaFubQzdUwwAoUUBBjtIvb6R33VDyeJ7hrJESNBdr4OH_if5Fef7HUaA2Ale6WkuIxqXe9qlbB0ytocSsKzObysxYmXmszKzZ7I31mf6uiW9MdRTneSgvsd9x_QXeHKc8 |
| CitedBy_id | crossref_primary_10_1016_j_compbiomed_2025_109874 crossref_primary_10_1016_j_istruc_2025_108598 crossref_primary_10_1109_JSTARS_2025_3600287 crossref_primary_10_1016_j_rser_2025_115895 crossref_primary_10_4103_joco_joco_172_24 crossref_primary_10_1038_s41467_024_49104_4 crossref_primary_10_1080_01431161_2024_2443620 crossref_primary_10_1016_j_pscia_2025_100080 crossref_primary_10_1007_s00138_025_01694_9 crossref_primary_10_1016_j_compbiomed_2024_107980 crossref_primary_10_1002_admt_202301316 crossref_primary_10_1093_bib_bbaf283 crossref_primary_10_1016_j_neucom_2023_126629 crossref_primary_10_1016_j_ascom_2024_100851 crossref_primary_10_1016_j_engappai_2025_110870 crossref_primary_10_3390_cancers17101604 crossref_primary_10_1002_aidi_202500024 crossref_primary_10_2196_71592 crossref_primary_10_3390_app15116001 crossref_primary_10_1007_s10334_025_01274_y crossref_primary_10_1016_j_ins_2024_120378 crossref_primary_10_1016_j_heliyon_2024_e37964 crossref_primary_10_1016_j_eswa_2024_125410 crossref_primary_10_3390_w17111718 crossref_primary_10_1007_s11042_024_20132_y crossref_primary_10_1016_j_asoc_2025_113378 crossref_primary_10_1016_j_neucom_2025_129607 crossref_primary_10_3390_s23218701 crossref_primary_10_1016_j_neucom_2025_131409 crossref_primary_10_3390_a17060267 crossref_primary_10_1109_JBHI_2024_3471907 crossref_primary_10_1016_j_tws_2025_113756 crossref_primary_10_1007_s10639_024_13111_1 crossref_primary_10_1007_s12083_025_02093_7 crossref_primary_10_3390_s25030602 crossref_primary_10_3390_rs16193624 crossref_primary_10_1109_TGRS_2024_3436841 crossref_primary_10_1007_s10618_025_01102_w crossref_primary_10_1002_advs_202505456 crossref_primary_10_1109_ACCESS_2025_3584401 crossref_primary_10_2196_55530 crossref_primary_10_1002_widm_70039 crossref_primary_10_1016_j_bspc_2024_106048 crossref_primary_10_1002_jeo2_12025 crossref_primary_10_1080_14787210_2025_2484284 crossref_primary_10_1007_s13735_025_00371_x crossref_primary_10_1016_j_measurement_2023_113294 crossref_primary_10_1016_j_talanta_2025_128483 crossref_primary_10_1016_j_jag_2023_103569 crossref_primary_10_3367_UFNr_2025_02_039872 crossref_primary_10_1016_j_biotechadv_2025_108698 crossref_primary_10_3389_fbioe_2024_1411680 crossref_primary_10_1016_j_engappai_2024_108503 crossref_primary_10_1038_s41598_025_01014_1 crossref_primary_10_1109_ACCESS_2024_3365349 crossref_primary_10_3390_electronics13183695 crossref_primary_10_1038_s41467_025_57541_y crossref_primary_10_1038_s41598_025_99000_0 crossref_primary_10_1016_j_optlastec_2024_111992 crossref_primary_10_1016_j_compmedimag_2024_102434 crossref_primary_10_1007_s44373_025_00042_w crossref_primary_10_2196_55094 crossref_primary_10_1016_j_smaim_2025_06_003 crossref_primary_10_3389_fbioe_2023_1285945 crossref_primary_10_1007_s11245_024_10046_7 crossref_primary_10_1016_j_ecoinf_2024_102989 crossref_primary_10_1109_JSTARS_2025_3591848 crossref_primary_10_1016_j_microc_2025_114814 crossref_primary_10_1016_j_cmpb_2024_108337 crossref_primary_10_1016_j_neucom_2024_129169 crossref_primary_10_3390_cancers16193285 crossref_primary_10_3390_technologies12070099 crossref_primary_10_1109_MWC_002_2400197 crossref_primary_10_1145_3736574 crossref_primary_10_3367_UFNe_2025_02_039872 crossref_primary_10_3389_frai_2024_1430984 crossref_primary_10_3390_cli13080159 crossref_primary_10_1007_s12530_024_09581_w crossref_primary_10_1038_s41598_025_08504_2 crossref_primary_10_1016_j_aei_2024_103079 crossref_primary_10_1016_j_aei_2025_103876 crossref_primary_10_1007_s00704_025_05698_3 crossref_primary_10_34133_hds_0282 crossref_primary_10_1016_j_engappai_2025_110042 crossref_primary_10_1016_j_anclin_2025_04_003 crossref_primary_10_1177_09596518251350353 crossref_primary_10_1007_s11004_025_10191_8 crossref_primary_10_1016_j_neunet_2024_107004 crossref_primary_10_1016_j_ins_2024_121516 crossref_primary_10_1038_s41598_024_78515_y crossref_primary_10_1038_s41598_025_87376_y crossref_primary_10_32604_cmc_2024_054644 crossref_primary_10_1016_j_compbiomed_2025_110510 crossref_primary_10_1016_j_eswa_2024_125843 crossref_primary_10_3390_app142310776 crossref_primary_10_1016_j_eswa_2024_125967 crossref_primary_10_1093_ehjdh_ztaf049 crossref_primary_10_3390_bdcc9080204 crossref_primary_10_3390_aerospace10080681 crossref_primary_10_1007_s11229_025_04923_w crossref_primary_10_1007_s12221_025_00942_z crossref_primary_10_1016_j_culher_2024_11_020 crossref_primary_10_3390_info15080491 crossref_primary_10_47992_IJCSBE_2581_6942_0370 crossref_primary_10_1088_1741_2552_ade402 crossref_primary_10_3390_bioengineering12050542 crossref_primary_10_1016_j_inffus_2024_102814 crossref_primary_10_1109_TNNLS_2024_3440498 |
| Cites_doi | 10.1117/1.JMI.9.6.064503 10.1145/3517246 10.1007/978-3-319-46466-4_5 10.1016/j.knosys.2021.107090 10.3390/e24040551 10.1109/CVPR46437.2021.01209 10.1109/CVPR.2016.91 10.1007/978-3-319-67558-9_34 10.1007/978-3-030-22475-2_1 10.1109/tpami.2022.3164911 10.1007/978-981-33-6984-9_41 10.3390/electronics9111930 10.24963/ijcai.2022/757 10.1162/tacl_a_00389 10.1007/978-3-030-92659-5_31 10.1109/CVPR.2016.278 10.1109/CVPR42600.2020.00975 10.1109/tip.2021.3120052 10.1145/3065386 10.3390/s22041599 10.1016/j.media.2019.101539 10.1109/access.2020.2973390 10.1109/CVPR52688.2022.01410 10.1007/s10994-019-05855-6 10.1109/TPAMI.2022.3199617 10.1186/s40537-016-0043-6 10.1109/iccv.2015.167 10.3390/informatics8030059 10.1109/cvpr42600.2020.00078 10.1109/ICSEngT.2019.8906400 10.1109/cvprw50498.2020.00485 10.1007/978-3-319-46493-0_35 10.1016/j.patter.2021.100410 10.1609/aaai.v34i05.6428 10.3390/technologies9010002 10.1109/TKDE.2009.191 10.18653/v1/2020.acl-main.740 10.1109/tpami.2020.2992393 10.1145/3397271.3401147 10.3390/e22111190 10.1109/cvpr.2019.01061 10.1109/CASE48305.2020.9217018 10.1007/978-3-319-46487-9_40 10.1109/access.2022.3165040 10.1093/nsr/nwx106 10.1109/cvpr.2014.81 10.1109/ACCESS.2020.3021469 10.1109/cvpr.2019.00512 10.1109/tcsvt.2022.3141051 10.1145/3550299 |
| ContentType | Journal Article |
| Copyright | The Author(s) under exclusive licence to International Center for Numerical Methods in Engineering (CIMNE) 2023 Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. The Author(s) under exclusive licence to International Center for Numerical Methods in Engineering (CIMNE) 2023, Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. The Author(s) under exclusive licence to International Center for Numerical Methods in Engineering (CIMNE) 2023. |
| Copyright_xml | – notice: The Author(s) under exclusive licence to International Center for Numerical Methods in Engineering (CIMNE) 2023 Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. – notice: The Author(s) under exclusive licence to International Center for Numerical Methods in Engineering (CIMNE) 2023, Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. – notice: The Author(s) under exclusive licence to International Center for Numerical Methods in Engineering (CIMNE) 2023. |
| DBID | AAYXX CITATION NPM 3V. 7WY 7WZ 7XB 87Z 8AL 8FE 8FG 8FK 8FL ABJCF ABUWG AFKRA ARAPS AZQEC BENPR BEZIV BGLVJ CCPQU DWQXO FRNLG F~G GNUQQ HCIFZ JQ2 K60 K6~ K7- L.- L6V M0C M0N M7S P5Z P62 PHGZM PHGZT PKEHL PQBIZ PQBZA PQEST PQGLB PQQKQ PQUKI PRINS PTHSS PYYUZ Q9U 7X8 5PM |
| DOI | 10.1007/s11831-023-09884-2 |
| DatabaseName | CrossRef PubMed ProQuest Central (Corporate) ABI/INFORM Collection ABI/INFORM Global (PDF only) ProQuest Central (purchase pre-March 2016) ABI/INFORM Collection Computing Database (Alumni Edition) ProQuest SciTech Collection ProQuest Technology Collection ProQuest Central (Alumni) (purchase pre-March 2016) ABI/INFORM Collection (Alumni) Materials Science & Engineering Collection ProQuest Central (Alumni) ProQuest Central UK/Ireland Advanced Technologies & Computer Science Collection ProQuest Central Essentials ProQuest Central Database Suite (ProQuest) Business Premium Collection Technology Collection ProQuest One Community College ProQuest Central Business Premium Collection (Alumni) ABI/INFORM Global (Corporate) ProQuest Central Student SciTech Collection (ProQuest) ProQuest Computer Science Collection ProQuest Business Collection (Alumni Edition) ProQuest Business Collection Computer Science Database (ProQuest) ABI/INFORM Professional Advanced ProQuest Engineering Collection ABI/INFORM Collection (ProQuest) Computing Database Engineering Database Advanced Technologies & Aerospace Database ProQuest Advanced Technologies & Aerospace Collection ProQuest Central Premium ProQuest One Academic (New) ProQuest One Academic Middle East (New) ProQuest One Business ProQuest One Business (Alumni) ProQuest One Academic Eastern Edition (DO NOT USE) ProQuest One Applied & Life Sciences ProQuest One Academic (retired) ProQuest One Academic UKI Edition ProQuest Central China Engineering Collection ABI/INFORM Collection China ProQuest Central Basic MEDLINE - Academic PubMed Central (Full Participant titles) |
| DatabaseTitle | CrossRef PubMed ABI/INFORM Global (Corporate) ProQuest Business Collection (Alumni Edition) ProQuest One Business Computer Science Database ProQuest Central Student Technology Collection ProQuest One Academic Middle East (New) ProQuest Advanced Technologies & Aerospace Collection ProQuest Central Essentials ProQuest Computer Science Collection ProQuest Central (Alumni Edition) SciTech Premium Collection ProQuest One Community College ProQuest Central China ABI/INFORM Complete ProQuest Central ABI/INFORM Professional Advanced ProQuest One Applied & Life Sciences ProQuest Engineering Collection ProQuest Central Korea ProQuest Central (New) ABI/INFORM Complete (Alumni Edition) Engineering Collection Advanced Technologies & Aerospace Collection Business Premium Collection ABI/INFORM Global ProQuest Computing Engineering Database ABI/INFORM Global (Alumni Edition) ProQuest Central Basic ProQuest Computing (Alumni Edition) ProQuest One Academic Eastern Edition ABI/INFORM China ProQuest Technology Collection ProQuest SciTech Collection ProQuest Business Collection Advanced Technologies & Aerospace Database ProQuest One Academic UKI Edition Materials Science & Engineering Collection ProQuest One Business (Alumni) ProQuest One Academic ProQuest Central (Alumni) ProQuest One Academic (New) Business Premium Collection (Alumni) MEDLINE - Academic |
| DatabaseTitleList | PubMed MEDLINE - Academic ABI/INFORM Global (Corporate) |
| 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: BENPR name: ProQuest Central url: https://www.proquest.com/central sourceTypes: Aggregation Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Applied Sciences Engineering |
| EISSN | 1886-1784 |
| EndPage | 2775 |
| ExternalDocumentID | PMC9857922 36713767 10_1007_s11831_023_09884_2 |
| Genre | Journal Article Review |
| GroupedDBID | -Y2 -~C -~X .4S .86 .DC .VR 06D 0R~ 0VY 1N0 1SB 203 23M 28- 29~ 2J2 2JN 2JY 2KG 2KM 2LR 2VQ 2~H 30V 4.4 406 408 40D 40E 5GY 5VS 67Z 6NX 7WY 8FE 8FG 8FL 8TC 8UJ 95- 95. 95~ 96X AAAVM AABHQ AACDK AAHNG AAIAL AAJBT AAJKR AANZL AAPKM AARHV AARTL AASML AATNV AATVU AAUYE AAWCG AAYIU AAYQN AAYTO AAYZH ABAKF ABBRH ABDBE ABDZT ABECU ABFTV ABHQN ABJCF ABJNI ABJOX ABKCH ABMNI ABMQK ABNWP ABQBU ABQSL ABSXP ABTEG ABTHY ABTKH ABTMW ABULA ABUWG ABWNU ABXPI ACAOD ACBXY ACDTI ACGFS ACHSB ACHXU ACIWK ACKNC ACMDZ ACMLO ACOKC ACOMO ACPIV ACZOJ ADHHG ADHIR ADHKG ADKNI ADKPE ADMLS ADRFC ADTPH ADURQ ADYFF ADZKW AEBTG AEFQL AEGAL AEGNC AEJHL AEJRE AEKMD AEMSY AENEX AEOHA AEPYU AESKC AETLH AEVLU AEXYK AFBBN AFDZB AFEXP AFGCZ AFKRA AFLOW AFQWF AFWTZ AFZKB AGAYW AGDGC AGGDS AGJBK AGMZJ AGQEE AGQMX AGQPQ AGRTI AGWIL AGWZB AGYKE AHAVH AHBYD AHKAY AHPBZ AHSBF AHYZX AIAKS AIGIU AIIXL AILAN AITGF AJBLW AJRNO AJZVZ ALMA_UNASSIGNED_HOLDINGS ALWAN AMKLP AMXSW AMYLF AMYQR AOCGG ARAPS ARCEE ARCSS ARMRJ ASPBG AVWKF AXYYD AYFIA AYJHY AZFZN AZQEC B-. BA0 BBWZM BDATZ BENPR BEZIV BGLVJ BGNMA BPHCQ CAG CCPQU COF CS3 CSCUP DDRTE DNIVK DPUIP DWQXO EBLON EBS EDO EIOEI EJD ESBYG FEDTE FERAY FFXSO FIGPU FINBP FNLPD FRNLG FRRFC FSGXE FWDCC GGCAI GGRSB GJIRD GNUQQ GNWQR GQ7 H13 HCIFZ HF~ HG5 HG6 HMJXF HRMNR HVGLF HZ~ I-F IJ- IKXTQ IWAJR IXC IXD IXE IZQ I~X I~Z J-C J0Z JBSCW K60 K6V K6~ K7- KDC KOV L6V LLZTM M0C M4Y M7S MA- MK~ N2Q NB0 NDZJH NF0 NPVJJ NQJWS NU0 O9- O93 O9G O9I O9J OAM P19 P2P P62 P9P PF0 PHGZT PQBIZ PQBZA PQQKQ PROAC PT4 PT5 PTHSS QOK QOS R4E R89 R9I RHV RNI RNS ROL RPX RSV RZK S16 S1Z S26 S27 S28 S3B SAP SCLPG SCV SDH SDM SEG SHX SISQX SJYHP SNE SNPRN SNX SOHCF SOJ SPISZ SRMVM SSLCW STPWE SZN T13 T16 TSG TSK TSV TUC TUS U2A UG4 UOJIU UTJUX UZXMN VC2 VFIZW W48 WK8 YLTOR Z45 ZMTXR ~EX AAYXX ABFSG ABRTQ ACSTC AEZWR AFFHD AFHIU AFOHR AHWEU AIXLP ATHPR CITATION JZLTJ PHGZM PQGLB NPM 3V. 7XB 8AL 8FK JQ2 L.- M0N PKEHL PQEST PQUKI PRINS Q9U 7X8 5PM |
| ID | FETCH-LOGICAL-c474t-a470d0840c99727242577f9ffc501c560e4a68df6ad87cdab736fc4299f93ddd3 |
| IEDL.DBID | K7- |
| ISICitedReferencesCount | 138 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000920400800004&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 1134-3060 1886-1784 |
| IngestDate | Tue Nov 04 02:06:35 EST 2025 Sun Nov 09 09:41:23 EST 2025 Mon Oct 06 18:40:23 EDT 2025 Thu Apr 03 06:57:34 EDT 2025 Tue Nov 18 22:38:47 EST 2025 Sat Nov 29 08:02:42 EST 2025 Thu Apr 10 08:01:53 EDT 2025 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 4 |
| Keywords | Self-supervised Un-supervised learning Supervised learning Contrastive learning Machine learning |
| Language | English |
| License | The Author(s) under exclusive licence to International Center for Numerical Methods in Engineering (CIMNE) 2023, Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c474t-a470d0840c99727242577f9ffc501c560e4a68df6ad87cdab736fc4299f93ddd3 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 ObjectType-Review-3 content type line 23 |
| ORCID | 0000-0003-0115-1620 |
| OpenAccessLink | https://pubmed.ncbi.nlm.nih.gov/PMC9857922 |
| PMID | 36713767 |
| PQID | 3256679342 |
| PQPubID | 1486352 |
| PageCount | 15 |
| ParticipantIDs | pubmedcentral_primary_oai_pubmedcentral_nih_gov_9857922 proquest_miscellaneous_2771087176 proquest_journals_3256679342 pubmed_primary_36713767 crossref_citationtrail_10_1007_s11831_023_09884_2 crossref_primary_10_1007_s11831_023_09884_2 springer_journals_10_1007_s11831_023_09884_2 |
| PublicationCentury | 2000 |
| PublicationDate | 2023-05-01 |
| PublicationDateYYYYMMDD | 2023-05-01 |
| PublicationDate_xml | – month: 05 year: 2023 text: 2023-05-01 day: 01 |
| PublicationDecade | 2020 |
| PublicationPlace | Dordrecht |
| PublicationPlace_xml | – name: Dordrecht – name: Netherlands |
| PublicationSubtitle | State of the Art Reviews |
| PublicationTitle | Archives of computational methods in engineering |
| PublicationTitleAbbrev | Arch Computat Methods Eng |
| PublicationTitleAlternate | Arch Comput Methods Eng |
| PublicationYear | 2023 |
| Publisher | Springer Netherlands Springer Nature B.V |
| Publisher_xml | – name: Springer Netherlands – name: Springer Nature B.V |
| References | 9884_CR30 9884_CR32 9884_CR35 9884_CR37 K Weiss (9884_CR15) 2016; 3 9884_CR36 9884_CR39 Y Sun (9884_CR38) 2020; 34 K He (9884_CR33) 2020; 2020 J Ding (9884_CR56) 2022 K Ohri (9884_CR2) 2021; 224 S Albelwi (9884_CR1) 2022; 24 A Chowdhury (9884_CR40) 2021; 8 9884_CR20 9884_CR22 9884_CR21 9884_CR23 9884_CR25 9884_CR28 W You (9884_CR31) 2022; 10 S Treneska (9884_CR19) 2022; 22 JEV Engelen (9884_CR9) 2019; 109 ZH Zhou (9884_CR10) 2017; 5 R Li (9884_CR24) 2022; 31 K Yun (9884_CR52) 2020; 8 9884_CR53 9884_CR11 9884_CR55 9884_CR13 9884_CR57 9884_CR12 9884_CR58 9884_CR17 9884_CR18 XB Nguyen (9884_CR46) 2020; 8 9884_CR3 SJ Pan (9884_CR16) 2010; 22 J Redmon (9884_CR29) 2016; 2016 A Jaiswal (9884_CR5) 2020; 9 9884_CR7 9884_CR8 9884_CR51 9884_CR50 9884_CR41 9884_CR44 A Kwasigroch (9884_CR43) 2020; 9 L Jing (9884_CR27) 2021; 43 9884_CR48 9884_CR47 Y Luo (9884_CR14) 2020; 22 9884_CR49 Y Jain (9884_CR54) 2022; 6 D Spathis (9884_CR45) 2022; 3 D Pathak (9884_CR6) 2016; 2016 L Tao (9884_CR4) 2022 A Krizhevsky (9884_CR26) 2017; 60 M Zhou (9884_CR34) 2021; 9 L Chen (9884_CR42) 2019; 58 |
| References_xml | – ident: 9884_CR44 doi: 10.1117/1.JMI.9.6.064503 – volume: 6 start-page: 1 issue: 1 year: 2022 ident: 9884_CR54 publication-title: Proc ACM Interact Mob Wearable Ubiquitous Technol doi: 10.1145/3517246 – ident: 9884_CR22 doi: 10.1007/978-3-319-46466-4_5 – volume: 224 start-page: 7090 year: 2021 ident: 9884_CR2 publication-title: Knowl-Based Syst doi: 10.1016/j.knosys.2021.107090 – volume: 24 start-page: 551 issue: 4 year: 2022 ident: 9884_CR1 publication-title: Entropy doi: 10.3390/e24040551 – ident: 9884_CR36 – ident: 9884_CR23 – ident: 9884_CR57 doi: 10.1109/CVPR46437.2021.01209 – volume: 2016 start-page: 779 year: 2016 ident: 9884_CR29 publication-title: IEEE Conf Comput Vis Pattern Recogn doi: 10.1109/CVPR.2016.91 – ident: 9884_CR41 doi: 10.1007/978-3-319-67558-9_34 – ident: 9884_CR8 doi: 10.1007/978-3-030-22475-2_1 – year: 2022 ident: 9884_CR56 publication-title: IEEE Trans Pattern Anal Mach Intell doi: 10.1109/tpami.2022.3164911 – ident: 9884_CR25 doi: 10.1007/978-981-33-6984-9_41 – volume: 9 start-page: 1930 issue: 11 year: 2020 ident: 9884_CR43 publication-title: Electronics doi: 10.3390/electronics9111930 – ident: 9884_CR32 doi: 10.24963/ijcai.2022/757 – ident: 9884_CR49 – volume: 9 start-page: 641 year: 2021 ident: 9884_CR34 publication-title: Trans Assoc Comput Linguist doi: 10.1162/tacl_a_00389 – ident: 9884_CR53 doi: 10.1007/978-3-030-92659-5_31 – volume: 2016 start-page: 2536 year: 2016 ident: 9884_CR6 publication-title: IEEE Conf Comput Vis Pattern Recogn doi: 10.1109/CVPR.2016.278 – volume: 2020 start-page: 9726 year: 2020 ident: 9884_CR33 publication-title: IEEE/CVF Conf Comput Vis Pattern Recogn doi: 10.1109/CVPR42600.2020.00975 – ident: 9884_CR58 – volume: 31 start-page: 513 year: 2022 ident: 9884_CR24 publication-title: IEEE Trans Image Process doi: 10.1109/tip.2021.3120052 – volume: 60 start-page: 84 issue: 6 year: 2017 ident: 9884_CR26 publication-title: Commun ACM doi: 10.1145/3065386 – ident: 9884_CR3 – volume: 22 start-page: 1599 issue: 4 year: 2022 ident: 9884_CR19 publication-title: Sensors doi: 10.3390/s22041599 – volume: 58 start-page: 101539 year: 2019 ident: 9884_CR42 publication-title: Med Image Anal doi: 10.1016/j.media.2019.101539 – volume: 8 start-page: 32502 year: 2020 ident: 9884_CR52 publication-title: IEEE Access doi: 10.1109/access.2020.2973390 – ident: 9884_CR55 doi: 10.1109/CVPR52688.2022.01410 – volume: 109 start-page: 373 issue: 2 year: 2019 ident: 9884_CR9 publication-title: Mach Learn doi: 10.1007/s10994-019-05855-6 – ident: 9884_CR48 doi: 10.1109/TPAMI.2022.3199617 – volume: 3 start-page: 9 year: 2016 ident: 9884_CR15 publication-title: J Big Data doi: 10.1186/s40537-016-0043-6 – ident: 9884_CR18 doi: 10.1109/iccv.2015.167 – volume: 8 start-page: 59 issue: 3 year: 2021 ident: 9884_CR40 publication-title: Informatics doi: 10.3390/informatics8030059 – ident: 9884_CR35 doi: 10.1109/cvpr42600.2020.00078 – ident: 9884_CR12 doi: 10.1109/ICSEngT.2019.8906400 – ident: 9884_CR50 doi: 10.1109/cvprw50498.2020.00485 – ident: 9884_CR7 doi: 10.1007/978-3-319-46493-0_35 – volume: 3 start-page: 1410 issue: 2 year: 2022 ident: 9884_CR45 publication-title: Patterns doi: 10.1016/j.patter.2021.100410 – volume: 34 start-page: 8968 issue: 05 year: 2020 ident: 9884_CR38 publication-title: Proc AAAI Conf Artifi Intell doi: 10.1609/aaai.v34i05.6428 – volume: 9 start-page: 2 issue: 1 year: 2020 ident: 9884_CR5 publication-title: Technologies doi: 10.3390/technologies9010002 – ident: 9884_CR47 – volume: 22 start-page: 1345 issue: 10 year: 2010 ident: 9884_CR16 publication-title: IEEE Trans Knowl Data Eng doi: 10.1109/TKDE.2009.191 – ident: 9884_CR37 doi: 10.18653/v1/2020.acl-main.740 – volume: 43 start-page: 4037 issue: 11 year: 2021 ident: 9884_CR27 publication-title: IEEE Trans Pattern Anal Mach Intell doi: 10.1109/tpami.2020.2992393 – ident: 9884_CR13 doi: 10.1145/3397271.3401147 – volume: 22 start-page: 1190 issue: 11 year: 2020 ident: 9884_CR14 publication-title: Entropy (Basel, Switzerland) doi: 10.3390/e22111190 – ident: 9884_CR20 doi: 10.1109/cvpr.2019.01061 – ident: 9884_CR39 – ident: 9884_CR21 doi: 10.1109/CASE48305.2020.9217018 – ident: 9884_CR17 doi: 10.1007/978-3-319-46487-9_40 – volume: 10 start-page: 36385 year: 2022 ident: 9884_CR31 publication-title: IEEE Access doi: 10.1109/access.2022.3165040 – volume: 5 start-page: 44 issue: 1 year: 2017 ident: 9884_CR10 publication-title: Natl Sci Rev doi: 10.1093/nsr/nwx106 – ident: 9884_CR28 doi: 10.1109/cvpr.2014.81 – volume: 8 start-page: 162973 year: 2020 ident: 9884_CR46 publication-title: IEEE Access doi: 10.1109/ACCESS.2020.3021469 – ident: 9884_CR11 – ident: 9884_CR51 doi: 10.1109/cvpr.2019.00512 – year: 2022 ident: 9884_CR4 publication-title: IEEE Trans Circ Syst Video Technol doi: 10.1109/tcsvt.2022.3141051 – ident: 9884_CR30 doi: 10.1145/3550299 |
| SSID | ssj0054992 |
| Score | 2.6622224 |
| SecondaryResourceType | review_article |
| Snippet | Machine learning has made significant advances in the field of image processing. The foundation of this success is supervised learning, which necessitates... |
| SourceID | pubmedcentral proquest pubmed crossref springer |
| SourceType | Open Access Repository Aggregation Database Index Database Enrichment Source Publisher |
| StartPage | 2761 |
| SubjectTerms | Algorithms Artificial intelligence Computer vision Datasets Engineering Image processing Image segmentation Labeling Literature reviews Machine learning Mathematical and Computational Engineering Natural language processing Object recognition Review Review Article Self-supervised learning Software Supervision Unstructured data Unsupervised learning |
| SummonAdditionalLinks | – databaseName: SpringerLink Contemporary dbid: RSV link: http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV3JTsMwEB0h4AAH9qVQUJC4gaU0cbxwQwjECSE2cYtcO4ZKVVqRlu9n7DotpYAEZ0_ieDxrxvMMcKw0tVrQjBijCkKTNiVt3sqIg7ox2rloG_vLJvjNjXh-lrehKayqT7vXJUlvqSfNbih9mPomKYmlEJSg4V1AdyecOt7dP9X21yU8vsbZSt0_fxaHVpnv3zHtjmZizNmjkl_qpd4NXa3-bwFrsBLCzuh8JCfrMFeUG7AaQtAoKHi1Acuf8Ak3AS1J15Jq2HcGpULCgMb6chadR_dDrTulHkSj8sIWPF5dPlxck3C7AtGU0wFRlMcmxvxOu95Z7lMPbqW1OotbGgOhgiomjGXKCK6NavOUWe3cl5WpMSbdhvmyVxa7EBnFkCyJmRaaSsOkiakSrgdXZFYmtgGtmsm5DtDj7gaMbj4BTXa8yZE3uedNnjTgZPxMfwS88St1s967PChhlacYzjG0PxSHj8bDqD6uJqLKojes8oRjiIVJI2cN2Blt9Xi6lGEGzxlvAJ8SgjGBg-aeHik7rx6iW4qMywTnPa1FYfJZP69i72_k-7CUeGlyxy-bMD94GxYHsKjfB53q7dArxQdCgAQz priority: 102 providerName: Springer Nature |
| Title | Self-supervised Learning: A Succinct Review |
| URI | https://link.springer.com/article/10.1007/s11831-023-09884-2 https://www.ncbi.nlm.nih.gov/pubmed/36713767 https://www.proquest.com/docview/3256679342 https://www.proquest.com/docview/2771087176 https://pubmed.ncbi.nlm.nih.gov/PMC9857922 |
| Volume | 30 |
| WOSCitedRecordID | wos000920400800004&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: PRVPQU databaseName: ABI/INFORM Collection customDbUrl: eissn: 1886-1784 dateEnd: 20241207 omitProxy: false ssIdentifier: ssj0054992 issn: 1134-3060 databaseCode: 7WY dateStart: 20230101 isFulltext: true titleUrlDefault: https://www.proquest.com/abicomplete providerName: ProQuest – providerCode: PRVPQU databaseName: ABI/INFORM Global customDbUrl: eissn: 1886-1784 dateEnd: 20241207 omitProxy: false ssIdentifier: ssj0054992 issn: 1134-3060 databaseCode: M0C dateStart: 20230101 isFulltext: true titleUrlDefault: https://search.proquest.com/abiglobal providerName: ProQuest – providerCode: PRVPQU databaseName: Advanced Technologies & Aerospace Database customDbUrl: eissn: 1886-1784 dateEnd: 20241207 omitProxy: false ssIdentifier: ssj0054992 issn: 1134-3060 databaseCode: P5Z dateStart: 20230101 isFulltext: true titleUrlDefault: https://search.proquest.com/hightechjournals providerName: ProQuest – providerCode: PRVPQU databaseName: Computer Science Database customDbUrl: eissn: 1886-1784 dateEnd: 20241207 omitProxy: false ssIdentifier: ssj0054992 issn: 1134-3060 databaseCode: K7- dateStart: 20230101 isFulltext: true titleUrlDefault: http://search.proquest.com/compscijour providerName: ProQuest – providerCode: PRVPQU databaseName: Engineering Database customDbUrl: eissn: 1886-1784 dateEnd: 20241207 omitProxy: false ssIdentifier: ssj0054992 issn: 1134-3060 databaseCode: M7S dateStart: 20230101 isFulltext: true titleUrlDefault: http://search.proquest.com providerName: ProQuest – providerCode: PRVPQU databaseName: ProQuest Central customDbUrl: eissn: 1886-1784 dateEnd: 20241207 omitProxy: false ssIdentifier: ssj0054992 issn: 1134-3060 databaseCode: BENPR dateStart: 20230101 isFulltext: true titleUrlDefault: https://www.proquest.com/central providerName: ProQuest – providerCode: PRVAVX databaseName: SpringerLINK Contemporary 1997-Present customDbUrl: eissn: 1886-1784 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0054992 issn: 1134-3060 databaseCode: RSV dateStart: 19970101 isFulltext: true titleUrlDefault: https://link.springer.com/search?facet-content-type=%22Journal%22 providerName: Springer Nature |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpR1Nb9Mw9IltHLgwvgnbqiBxA4vEcfzBZRrTJiREVa18FC6Ra8dQaUrL0vL78XOcVt3ELlzexY4c-337-b0H8Eob5oxkJbFW14TRKSNTkZcES91YgyraZaHZhBgO5WSiRvHCrY3PKnuZGAS1nRu8I39beN3MPTExerz4TbBrFEZXYwuNHdjLKc2Rzj8K0ktidH1CtDMv8PafZzFppkud87TsHWlakExJyQjdVkw3rM2bjyavRU6DQjrf_9-tPID70RRNTzraeQh36uYR7EezNI1M3z4GL08uHWlXCxQrrR-KNVl_vktP0vHKmFljlmkXZHgCX87PPp9-ILHHAjFMsCXRTGQ2816ewQxaERwQ4ZRzpsxy482hmmkurePaSmGsnoqCO4NKzKnCWls8hd1m3tTPIbWa-2k040YapixXNmNaYiauLJ2iLoG8P-DKxALk2AfjstqUTkakVB4pVUBKRRN4vf5m0ZXfuHX2YX_gVWTFttqcdgIv18OeiTAyopt6vmorKryh5V1HwRN41qF5vVzBvR8vuEhAbBHAegIW6N4eaWa_QqFuJUuhqF_3TU8qm9_69y5e3L6LA7hHA9nio8tD2F1ereojuGv-LGft1QB2xLfvCCdyAHvvz4aji0FgCg8_ZacIxdjDUfnDw4vx179O3xDe |
| linkProvider | ProQuest |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1Lb9QwEB6VggQXyruBAkGCE1g4juMHUlVVhapVywqJIvVmsnZMV6qyS7ML4k_1N9bjJLtaKnrrgbOd2I6_-TyT8cwAvC4t91bxgjhXVoSzISdDmRUEU904i0e0p7HYhBwM1PGx_rIC530sDF6r7DkxErUbW_xH_j4PZ7MIYOJsa_KTYNUo9K72JTRaWBxUf34Hk63Z3P8Y9vcNY7ufjnb2SFdVgFgu-ZSUXFJHg11jMWZURpVbeu29LWhmgwJQ8VIo50XplLSuHMpceIu07XXunMvDe2_ATc6DOOBVQbrTMz-aWtG7muXobRC0C9JpQ_WC7ATDneWEaqU4YcsH4SXt9vIlzb88tfEA3F373z7dPbjbqdrpdisb92Glqh_AWqd2px2pNQ8h8OWpJ81sgrTZhKYu5-yPD-l2-nVm7ai207R1ojyCb9cy5cewWo_rah1SV4rQjVFhleXaCe0oLxVGGqvCa-YTyPoNNbZLsI51Pk7NIjU0gsAEEJgIAsMSeDt_ZtKmF7my90a_waajmsYsdjeBV_PmQBLo-SnrajxrDJNBkQymsRQJPGlhNR8uFzLDlD4JyCXAzTtgAvLllnp0EhORa1VIzcK473poLqb171U8vXoVL-H23tHnQ3O4Pzh4BndYFBm8YLoBq9OzWfUcbtlf01Fz9iIKXwrfrxuyF3HFZoA |
| linkToPdf | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV3dT9wwDLcQoGl74HNjx8foJN4gotem-eANAachphMSY-ItyiXNOAmVE-3t75-TtnccDCTEc9ymcWzHruOfAfa0oc4ImhFrdU5oMqBkwLsZ8VA31vgj2sWh2QTv98XNjbx8VMUfbru3Kcm6psGjNBXV4ci6w2nhG0oihsFJSmIpBCVohBeobxrk4_Wr360t9sFPyHd2U___n8VN2cz_3zF7ND3zN59fm3ySOw1HUm_5_YtZgaXGHY2Oa_lZhbm8WIPlxjWNGsUv1-DTI9zCdUALc-dIOR55Q1MiYYPS-ucoOo6uxsYMC1NFddrhM1z3zn6d_CBN1wViKKcV0ZTHNsa4z_iaWh5CEu6kcyaLuwYdpJxqJqxj2gpurB7wlDnjjzUnU2tt-gXmi_si_wqR1QzJkpgZYai0TNqYauFrc0XmZOI60G0ZrkwDSe47Y9ypKZiy541C3qjAG5V0YH_yzKgG5HiVervdR9UoZ6lSdPMY2iWKw98nw6hWPleii_x-XKqEo-uFwSRnHdiot30yXcowsueMd4DPCMSEwEN2z44Uw9sA3S1FxmWC8x60YjH9rJdXsfk28l34cHnaUz_P-xdb8DEJguVvaG7DfPUwzndg0fythuXDt6Ar_wAW6w_7 |
| 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=Self-supervised+Learning%3A+A+Succinct+Review&rft.jtitle=Archives+of+computational+methods+in+engineering&rft.au=Rani%2C+Veenu&rft.au=Nabi%2C+Syed+Tufael&rft.au=Kumar%2C+Munish&rft.au=Mittal%2C+Ajay&rft.date=2023-05-01&rft.pub=Springer+Nature+B.V&rft.issn=1134-3060&rft.eissn=1886-1784&rft.volume=30&rft.issue=4&rft.spage=2761&rft.epage=2775&rft_id=info:doi/10.1007%2Fs11831-023-09884-2 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1134-3060&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1134-3060&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1134-3060&client=summon |