{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,11]],"date-time":"2026-01-11T18:16:09Z","timestamp":1768155369944,"version":"3.49.0"},"reference-count":62,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2024,2,1]],"date-time":"2024-02-01T00:00:00Z","timestamp":1706745600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,2,1]],"date-time":"2024-02-01T00:00:00Z","timestamp":1706745600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Appl Intell"],"published-print":{"date-parts":[[2024,2]]},"DOI":"10.1007\/s10489-024-05340-1","type":"journal-article","created":{"date-parts":[[2024,2,27]],"date-time":"2024-02-27T06:02:21Z","timestamp":1709013741000},"page":"2976-2997","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Self-Supervison with data-augmentation improves few-shot learning"],"prefix":"10.1007","volume":"54","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-2520-6419","authenticated-orcid":false,"given":"Prashant","family":"Kumar","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7960-4127","authenticated-orcid":false,"given":"Durga","family":"Toshniwal","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,2,27]]},"reference":[{"key":"5340_CR1","unstructured":"Finn C, Abbeel P, Levine S (2017) Model-agnostic meta-learning for fast adaptation of deep networks. In: International conference on machine learning, pp 1126\u20131135. PMLR"},{"key":"5340_CR2","doi-asserted-by":"crossref","unstructured":"Gidaris S, Bursuc A, Komodakis N, P\u00e9rez P, Cord M (2019) Boosting few-shot visual learning with self-supervision. In: Proceedings of the IEEE\/CVF international conference on computer vision, pp 8059\u20138068","DOI":"10.1109\/ICCV.2019.00815"},{"key":"5340_CR3","unstructured":"Snell J, Swersky K, Zemel R (2017) Prototypical networks for few-shot learning. Advances in neural information processing systems 30"},{"key":"5340_CR4","doi-asserted-by":"crossref","unstructured":"Lee K, Maji S, Ravichandran A, Soatto S (2019) Meta-learning with differentiable convex optimization. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 10657\u201310665","DOI":"10.1109\/CVPR.2019.01091"},{"key":"5340_CR5","unstructured":"Ren M, Triantafillou E, Ravi S, Snell J, Swersky K, Tenenbaum JB, Larochelle H, Zemel RS (2018) Meta-learning for semi-supervised few-shot classification. arXiv:1803.00676"},{"key":"5340_CR6","doi-asserted-by":"crossref","unstructured":"Doersch C, Gupta A, Efros AA (2015) Unsupervised visual representation learning by context prediction. In: Proceedings of the IEEE international conference on computer vision, pp 1422\u20131430","DOI":"10.1109\/ICCV.2015.167"},{"key":"5340_CR7","doi-asserted-by":"crossref","unstructured":"Wang B, Li L, Verma M, Nakashima Y, Kawasaki R, Nagahara H (2023) Match them up: visually explainable few-shot image classification. Applied Intelligence, pp 1\u201322","DOI":"10.1007\/s10489-022-04072-4"},{"key":"5340_CR8","doi-asserted-by":"publisher","first-page":"110800","DOI":"10.1016\/j.knosys.2023.110800","volume":"277","author":"P Tian","year":"2023","unstructured":"Tian P, Yu H (2023) Can we improve meta-learning model in few-shot learning by aligning data distributions? Knowl Based Syst 277:110800","journal-title":"Knowl Based Syst"},{"key":"5340_CR9","doi-asserted-by":"publisher","first-page":"996","DOI":"10.1016\/j.ins.2022.07.022","volume":"608","author":"H Yu","year":"2022","unstructured":"Yu H, Zhang Q, Liu T, Lu J, Wen Y, Zhang G (2022) Meta-add: A meta-learning based pre-trained model for concept drift active detection. Inf Sci 608:996\u20131009","journal-title":"Inf Sci"},{"key":"5340_CR10","doi-asserted-by":"crossref","unstructured":"Noroozi M, Favaro P (2016) Unsupervised learning of visual representations by solving jigsaw puzzles. European Conference on Computer Vision, pp\u00a069\u201384","DOI":"10.1007\/978-3-319-46466-4_5"},{"key":"5340_CR11","doi-asserted-by":"crossref","unstructured":"Noroozi M, Pirsiavash H (2017) Representation learning by learning to count. In: Proceedings of the IEEE international conference on computer vision, pp 5898\u20135906","DOI":"10.1109\/ICCV.2017.628"},{"key":"5340_CR12","doi-asserted-by":"crossref","unstructured":"Zhang R, Isola P, Efros AA (2016) Colorful image colorization. In: European conference on computer vision","DOI":"10.1007\/978-3-319-46487-9_40"},{"key":"5340_CR13","doi-asserted-by":"crossref","unstructured":"Fini E, Astolfi P, Alahari K, Alameda-Pineda X, Mairal J, Nabi M, Ricci E (2023) Semi-supervised learning made simple with self-supervised clustering. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 3187\u20133197","DOI":"10.1109\/CVPR52729.2023.00311"},{"key":"5340_CR14","unstructured":"Rajasegaran J, Khan S, Hayat M, Khan FS, Shah M (2020) Self-supervised knowledge distillation for few-shot learning. arXiv:2006.09785"},{"key":"5340_CR15","doi-asserted-by":"crossref","unstructured":"Tian Y, Krishnan D, Isola P (2020) Contrastive multiview coding. In: European conference on computer vision, pp 776\u2013794. Springer","DOI":"10.1007\/978-3-030-58621-8_45"},{"key":"5340_CR16","doi-asserted-by":"publisher","first-page":"107840","DOI":"10.1016\/j.knosys.2021.107840","volume":"238","author":"P Singh","year":"2022","unstructured":"Singh P, Mazumder P (2022) Dual class representation learning for few-shot image classification. Knowl Based Syst 238:107840","journal-title":"Knowl Based Syst"},{"key":"5340_CR17","doi-asserted-by":"crossref","unstructured":"Yang Z, Wang J, Zhu Y (2022) Few-shot classification with contrastive learning. In: European Conference on Computer Vision, pp 293\u2013309. Springer","DOI":"10.1007\/978-3-031-20044-1_17"},{"key":"5340_CR18","doi-asserted-by":"crossref","unstructured":"Tian Y, Wang Y, Krishnan D, Tenenbaum J.B, Isola P (2020) Rethinking few-shot image classification: a good embedding is all you need. In: European conference on computer vision, pp 266\u2013282. Springer","DOI":"10.1007\/978-3-030-58568-6_16"},{"key":"5340_CR19","unstructured":"Howard AG (2013) Some improvements on deep convolutional neural network based image classification. arXiv:1312.5402"},{"key":"5340_CR20","doi-asserted-by":"crossref","unstructured":"Guo Y, Cheung N-M (2020) Attentive weights generation for few shot learning via information maximization. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 13499\u201313508","DOI":"10.1109\/CVPR42600.2020.01351"},{"key":"5340_CR21","doi-asserted-by":"publisher","first-page":"13","DOI":"10.1016\/j.neucom.2020.07.128","volume":"423","author":"Z Ji","year":"2021","unstructured":"Ji Z, Chai X, Yu Y, Zhang Z (2021) Reweighting and information-guidance networks for few-shot learning. Neurocomputing 423:13\u201323","journal-title":"Neurocomputing"},{"key":"5340_CR22","doi-asserted-by":"publisher","first-page":"200","DOI":"10.1016\/j.neucom.2021.02.024","volume":"442","author":"H Song","year":"2021","unstructured":"Song H, Torres MT, \u00d6zcan E, Triguero I (2021) L2ae-d: Learning to aggregate embeddings for few-shot learning with meta-level dropout. Neurocomputing 442:200\u2013208","journal-title":"Neurocomputing"},{"key":"5340_CR23","unstructured":"Chen L-C, Papandreou G, Kokkinos I, Murphy K, Yuille AL (2014) Semantic image segmentation with deep convolutional nets and fully connected crfs. arXiv:1412.7062"},{"key":"5340_CR24","unstructured":"Oreshkin B, Rodr\u00edguez\u00a0L\u00f3pez P, Lacoste A (2018) Tadam: Task dependent adaptive metric for improved few-shot learning. Advances in neural information processing systems 31"},{"key":"5340_CR25","doi-asserted-by":"crossref","unstructured":"Sung F, Yang Y, Zhang L, Xiang T, Torr P.H, Hospedales TM (2018) Learning to compare: Relation network for few-shot learning. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1199\u20131208","DOI":"10.1109\/CVPR.2018.00131"},{"key":"5340_CR26","doi-asserted-by":"crossref","unstructured":"Gidaris S, Komodakis N (2018) Dynamic few-shot visual learning without forgetting. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4367\u20134375","DOI":"10.1109\/CVPR.2018.00459"},{"key":"5340_CR27","unstructured":"Bertinetto L, Henriques JF, Torr PH, Vedaldi A (2018) Meta-learning with differentiable closed-form solvers"},{"key":"5340_CR28","unstructured":"Rusu AA, Rao D, Sygnowski J, Vinyals O, Pascanu R, Osindero S, Hadsell R (2018) Meta-learning with latent embedding optimization. arXiv:1807.05960"},{"issue":"1","key":"5340_CR29","doi-asserted-by":"publisher","first-page":"240","DOI":"10.1109\/TCSVT.2021.3058098","volume":"32","author":"C Chen","year":"2021","unstructured":"Chen C, Li K, Wei W, Zhou JT, Zeng Z (2021) Hierarchical graph neural networks for few-shot learning. IEEE Trans Circuits Syst Video Technol 32(1):240\u2013252","journal-title":"IEEE Trans Circuits Syst Video Technol"},{"issue":"3","key":"5340_CR30","doi-asserted-by":"publisher","first-page":"1091","DOI":"10.1109\/TCSVT.2020.2995754","volume":"31","author":"W Jiang","year":"2020","unstructured":"Jiang W, Huang K, Geng J, Deng X (2020) Multi-scale metric learning for few-shot learning. IEEE Trans Circuits Syst Video Technol 31(3):1091\u20131102","journal-title":"IEEE Trans Circuits Syst Video Technol"},{"key":"5340_CR31","doi-asserted-by":"crossref","unstructured":"Huang H, Zhang J, Yu L, Zhang J, Wu Q, Xu C (2021) Toan: Target-oriented alignment network for fine-grained image categorization with few labeled samples. IEEE Transactions on Circuits and Systems for Video Technology","DOI":"10.1109\/TCSVT.2021.3065693"},{"key":"5340_CR32","doi-asserted-by":"publisher","first-page":"9594","DOI":"10.1609\/aaai.v35i11.17155","volume":"35","author":"Z Shen","year":"2021","unstructured":"Shen Z, Liu Z, Qin J, Savvides M, Cheng K-T (2021) Partial is better than all: Revisiting fine-tuning strategy for few-shot learning. Proceedings of the AAAI conference on artificial intelligence 35:9594\u20139602","journal-title":"Proceedings of the AAAI conference on artificial intelligence"},{"key":"5340_CR33","unstructured":"Xu W, Wang H, Tu Z, et al (2020) Attentional constellation nets for few-shot learning. In: International conference on learning representations"},{"key":"5340_CR34","doi-asserted-by":"publisher","first-page":"106647","DOI":"10.1016\/j.knosys.2020.106647","volume":"212","author":"M Abdel-Basset","year":"2021","unstructured":"Abdel-Basset M, Chang V, Hawash H, Chakrabortty RK, Ryan M (2021) Fss-2019-ncov: A deep learning architecture for semi-supervised few-shot segmentation of covid-19 infection. Knowl Based Syst 212:106647","journal-title":"Knowl Based Syst"},{"key":"5340_CR35","doi-asserted-by":"publisher","first-page":"64","DOI":"10.1016\/j.neucom.2021.01.123","volume":"442","author":"M Li","year":"2021","unstructured":"Li M, Wang R, Yang J, Xue L, Hu M (2021) Multi-domain few-shot image recognition with knowledge transfer. Neurocomputing 442:64\u201372","journal-title":"Neurocomputing"},{"key":"5340_CR36","doi-asserted-by":"crossref","unstructured":"He K, Fan H, Wu Y, Xie S, Girshick R (2020) Momentum contrast for unsupervised visual representation learning. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 9729\u20139738","DOI":"10.1109\/CVPR42600.2020.00975"},{"key":"5340_CR37","unstructured":"Chen T, Kornblith S, Norouzi M, Hinton G (2020) A simple framework for contrastive learning of visual representations. In: International conference on machine learning, pp 1597\u20131607. PMLR"},{"key":"5340_CR38","doi-asserted-by":"crossref","unstructured":"Mazumder P, Singh P, Namboodiri VP (2022) Few-shot image classification with composite rotation based self-supervised auxiliary task. Neurocomputing","DOI":"10.1016\/j.neucom.2022.02.044"},{"key":"5340_CR39","doi-asserted-by":"crossref","unstructured":"Ji Z, Zou X, Huang T, Wu S (2019) Unsupervised few-shot learning via self-supervised training. arXiv:1912.12178","DOI":"10.3389\/fncom.2020.00083"},{"key":"5340_CR40","doi-asserted-by":"crossref","unstructured":"Amac MS, Sencan A, Baran B, Ikizler-Cinbis N, Cinbis RG (2022) Masksplit: Self-supervised meta-learning for few-shot semantic segmentation. In: Proceedings of the IEEE\/CVF winter conference on applications of computer vision, pp 1067\u20131077","DOI":"10.1109\/WACV51458.2022.00050"},{"key":"5340_CR41","unstructured":"Gidaris S, Singh P, Komodakis N (2018) Unsupervised representation learning by predicting image rotations. arXiv:1803.07728"},{"key":"5340_CR42","doi-asserted-by":"crossref","unstructured":"Qi H, Brown M, Lowe DG (2018) Low-shot learning with imprinted weights. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 5822\u20135830","DOI":"10.1109\/CVPR.2018.00610"},{"key":"5340_CR43","unstructured":"Vinyals O, Blundell C, Lillicrap T, Wierstra D et al (2016) Matching networks for one shot learning. Advances in neural information processing systems 29"},{"key":"5340_CR44","doi-asserted-by":"publisher","first-page":"106609","DOI":"10.1016\/j.knosys.2020.106609","volume":"213","author":"Y Qin","year":"2021","unstructured":"Qin Y, Zhang W, Zhao C, Wang Z, Zhu X, Shi J, Qi G, Lei Z (2021) Prior-knowledge and attention based meta-learning for few-shot learning. Knowl Based Syst 213:106609","journal-title":"Knowl Based Syst"},{"key":"5340_CR45","doi-asserted-by":"publisher","first-page":"109235","DOI":"10.1016\/j.patcog.2022.109235","volume":"136","author":"L Zhang","year":"2023","unstructured":"Zhang L, Zhou F, Wei W, Zhang Y (2023) Meta-hallucinating prototype for few-shot learning promotion. Pattern Recognit 136:109235","journal-title":"Pattern Recognit"},{"key":"5340_CR46","doi-asserted-by":"crossref","unstructured":"Yang F, Wang R, Chen X (2023) Semantic guided latent parts embedding for few-shot learning. In: Proceedings of the IEEE\/CVF winter conference on applications of computer vision, pp 5447\u20135457","DOI":"10.1109\/WACV56688.2023.00541"},{"key":"5340_CR47","doi-asserted-by":"crossref","unstructured":"Ravichandran A, Bhotika R, Soatto S (2019) Few-shot learning with embedded class models and shot-free meta training. In: Proceedings of the IEEE\/CVF international conference on computer vision, pp 331\u2013339","DOI":"10.1109\/ICCV.2019.00042"},{"key":"5340_CR48","doi-asserted-by":"crossref","unstructured":"Chen M, Fang Y, Wang X, Luo H, Geng Y, Zhang X, Huang C, Liu W, Wang B (2020) Diversity transfer network for few-shot learning. In: Proceedings of the AAAI conference on artificial intelligence vol\u00a034, pp\u00a010559\u201310566","DOI":"10.1609\/aaai.v34i07.6628"},{"key":"5340_CR49","unstructured":"Dhillon GS, Chaudhari P, Ravichandran A, Soatto S (2020) A baseline for few-shot image classification. In: International conference on learning representations"},{"key":"5340_CR50","doi-asserted-by":"crossref","unstructured":"Wang Y, Xu C, Liu C, Zhang L, Fu Y (2020) Instance credibility inference for few-shot learning. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 12836\u201312845","DOI":"10.1109\/CVPR42600.2020.01285"},{"key":"5340_CR51","doi-asserted-by":"crossref","unstructured":"Lu Y, Wen L, Liu J, Liu Y, Tian X (2022) Self-supervision can be a good few-shot learner. In: European conference on computer vision, pp 740\u2013758. Springer","DOI":"10.1007\/978-3-031-19800-7_43"},{"key":"5340_CR52","doi-asserted-by":"crossref","unstructured":"Chen J, Zhan L-M, Wu X-M, Chung F-l (2020) Variational metric scaling for metric-based meta-learning. In: Proceedings of the AAAI conference on artificial intelligence, vol 34, pp\u00a03478\u20133485","DOI":"10.1609\/aaai.v34i04.5752"},{"key":"5340_CR53","unstructured":"Liu Y, Lee J, Park M, Kim S, Yang Y (2018) Transductive propagation network for few-shot learning"},{"issue":"8","key":"5340_CR54","doi-asserted-by":"publisher","first-page":"3458","DOI":"10.1109\/TNNLS.2020.3011526","volume":"32","author":"N Lai","year":"2020","unstructured":"Lai N, Kan M, Han C, Song X, Shan S (2020) Learning to learn adaptive classifier-predictor for few-shot learning. IEEE Trans Neural Netw Learn Syst 32(8):3458\u20133470","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"5340_CR55","unstructured":"Flennerhag S, Rusu AA, Pascanu R, Visin F, Yin H, Hadsell R (2020) Meta-learning with warped gradient descent. In: International conference on learning representations"},{"key":"5340_CR56","doi-asserted-by":"crossref","unstructured":"Zhang H, Zhang J, Koniusz P (2019) Few-shot learning via saliency-guided hallucination of samples. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 2770\u20132779","DOI":"10.1109\/CVPR.2019.00288"},{"key":"5340_CR57","doi-asserted-by":"crossref","unstructured":"Sun Q, Liu Y, Chua T-S, Schiele B (2019) Meta-transfer learning for few-shot learning. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 403\u2013412","DOI":"10.1109\/CVPR.2019.00049"},{"issue":"4","key":"5340_CR58","doi-asserted-by":"publisher","first-page":"1433","DOI":"10.1109\/TNNLS.2020.2984710","volume":"32","author":"J Lu","year":"2020","unstructured":"Lu J, Jin S, Liang J, Zhang C (2020) Robust few-shot learning for user-provided data. IEEE Trans Neural Netw Learn Syst 32(4):1433\u20131447","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"5340_CR59","doi-asserted-by":"crossref","unstructured":"Lifchitz Y, Avrithis Y, Picard S, Bursuc A (2019) Dense classification and implanting for few-shot learning. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 9258\u20139267","DOI":"10.1109\/CVPR.2019.00948"},{"key":"5340_CR60","doi-asserted-by":"crossref","unstructured":"Chen X, He K (2021) Exploring simple siamese representation learning. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 15750\u201315758","DOI":"10.1109\/CVPR46437.2021.01549"},{"key":"5340_CR61","unstructured":"Marquez RG, Berens P, Kobak D (2022) Two-dimensional visualization of large document libraries using t-sne. In: ICLR 2022 workshop on geometrical and topological representation learning"},{"key":"5340_CR62","doi-asserted-by":"crossref","unstructured":"Selvaraju RR, Cogswell M, Das A, Vedantam R, Parikh D, Batra D (2017) Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE international conference on computer vision, pp 618\u2013626","DOI":"10.1109\/ICCV.2017.74"}],"container-title":["Applied Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-024-05340-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10489-024-05340-1\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-024-05340-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,4,3]],"date-time":"2024-04-03T13:18:12Z","timestamp":1712150292000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10489-024-05340-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,2]]},"references-count":62,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2024,2]]}},"alternative-id":["5340"],"URL":"https:\/\/doi.org\/10.1007\/s10489-024-05340-1","relation":{},"ISSN":["0924-669X","1573-7497"],"issn-type":[{"value":"0924-669X","type":"print"},{"value":"1573-7497","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,2]]},"assertion":[{"value":"4 February 2024","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 February 2024","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing of interest"}},{"value":"In this paper, the dataset names are mentioned clearly, and it is stated that these datasets are publicly available. Additionally, it is stated that no ethical approval or informed consent was required for the usage of these datasets.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical and informed consent for data used"}}]}}