{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,13]],"date-time":"2026-03-13T11:46:27Z","timestamp":1773402387017,"version":"3.50.1"},"reference-count":35,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2024,5,14]],"date-time":"2024-05-14T00:00:00Z","timestamp":1715644800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,5,14]],"date-time":"2024-05-14T00:00:00Z","timestamp":1715644800000},"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":["Pattern Anal Applic"],"published-print":{"date-parts":[[2024,6]]},"DOI":"10.1007\/s10044-024-01271-2","type":"journal-article","created":{"date-parts":[[2024,5,14]],"date-time":"2024-05-14T11:01:47Z","timestamp":1715684507000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["MAC: a meta-learning approach for feature learning and recombination"],"prefix":"10.1007","volume":"27","author":[{"given":"Sambhavi","family":"Tiwari","sequence":"first","affiliation":[]},{"given":"Manas","family":"Gogoi","sequence":"additional","affiliation":[]},{"given":"Shekhar","family":"Verma","sequence":"additional","affiliation":[]},{"given":"Krishna Pratap","family":"Singh","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,5,14]]},"reference":[{"issue":"20","key":"1271_CR1","doi-asserted-by":"publisher","first-page":"14611","DOI":"10.1007\/s00521-021-05841-x","volume":"35","author":"M Wo\u017aniak","year":"2023","unstructured":"Wo\u017aniak M, Si\u0142ka J, Wieczorek M (2023) Deep neural network correlation learning mechanism for ct brain tumor detection. Neural Comput Appl 35(20):14611\u201314626","journal-title":"Neural Comput Appl"},{"key":"1271_CR2","doi-asserted-by":"publisher","first-page":"489","DOI":"10.1016\/j.future.2022.12.004","volume":"141","author":"M Wo\u017aniak","year":"2023","unstructured":"Wo\u017aniak M, Wieczorek M, Si\u0142ka J (2023) Bilstm deep neural network model for imbalanced medical data of IoT systems. Futur Gener Comput Syst 141:489\u2013499","journal-title":"Futur Gener Comput Syst"},{"key":"1271_CR3","doi-asserted-by":"crossref","unstructured":"Abe M, Nakayama H (2018) Deep learning for forecasting stock returns in the cross-section. In: Advances in knowledge discovery and data mining: 22nd Pacific-Asia conference, PAKDD 2018, Melbourne, VIC, Australia, June 3-6, 2018, Proceedings, Part I 22, pp 273\u2013284. Springer","DOI":"10.1007\/978-3-319-93034-3_22"},{"key":"1271_CR4","unstructured":"Bojarski M, Del\u00a0Testa D, Dworakowski D, Firner B, Flepp B, Goyal P, Jackel LD, Monfort M, Muller U, Zhang J et\u00a0al (2016) End to end learning for self-driving cars. arXiv preprint. arXiv:1604.07316"},{"key":"1271_CR5","first-page":"1","volume":"30","author":"A Vaswani","year":"2017","unstructured":"Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser \u0141, Polosukhin I (2017) Attention is all you need. Adv Neural Inf Process Syst 30:1\u20132","journal-title":"Adv Neural Inf Process Syst"},{"key":"1271_CR6","doi-asserted-by":"crossref","unstructured":"Wo\u017aniak M, Wieczorek M, Si\u0142ka J (2022) Deep neural network with transfer learning in remote object detection from drone. In: Proceedings of the 5th international ACM mobicom workshop on drone assisted wireless communications for 5G and beyond, pp 121\u2013126","DOI":"10.1145\/3555661.3560875"},{"key":"1271_CR7","doi-asserted-by":"crossref","unstructured":"Ambalavanan V et\u00a0al (2020) Cyber threats detection and mitigation using machine learning. In: Handbook of research on machine and deep learning applications for cyber security, pp 132\u2013149. IGI Global","DOI":"10.4018\/978-1-5225-9611-0.ch007"},{"key":"1271_CR8","unstructured":"Koch G, Zemel R, Salakhutdinov R et\u00a0al (2015) Siamese neural networks for one-shot image recognition. In: ICML deep learning workshop, vol.\u00a02, Lille"},{"key":"1271_CR9","unstructured":"Vinyals O, Blundell C, Lillicrap T, Wierstra D et al (2016) Matching networks for one shot learning. Adv Neural Inf Process Syst 29"},{"key":"1271_CR10","unstructured":"Snell J, Swersky K, Zemel R (2017) Prototypical networks for few-shot learning. Adv Neural Inf Process Syst 30"},{"key":"1271_CR11","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":"1271_CR12","unstructured":"Santoro A, Bartunov S, Botvinick M, Wierstra D, Lillicrap T (2016) Meta-learning with memory-augmented neural networks. In: International conference on machine learning, pp 1842\u20131850. PMLR"},{"key":"1271_CR13","unstructured":"Ravi S, Larochelle H (2016) Optimization as a model for few-shot learning. In: International conference on learning representations"},{"key":"1271_CR14","unstructured":"Nichol A, Achiam J, Schulman J (2018) On first-order meta-learning algorithms. arXiv preprint. arXiv:1803.02999,"},{"key":"1271_CR15","unstructured":"Raghu A, Raghu M, Bengio S, Vinyals O (2019) Rapid learning or feature reuse? towards understanding the effectiveness of maml. arXiv preprint. arXiv:1909.09157,"},{"key":"1271_CR16","unstructured":"Bengio S, Bengio Y, Cloutier J, Gecsei J (1995) On the optimization of a synaptic learning rule. In: Preprints conference optimality in artificial and biological neural networks, vol.\u00a02"},{"key":"1271_CR17","doi-asserted-by":"crossref","unstructured":"Hochreiter S, Younger AS, Conwell PR (2001) Learning to learn using gradient descent. In: International conference on artificial neural networks, pp 87\u201394. Springer","DOI":"10.1007\/3-540-44668-0_13"},{"key":"1271_CR18","unstructured":"Munkhdalai T, Yu H (2017) Meta networks. In: International conference on machine learning, pp 2554\u20132563. PMLR"},{"key":"1271_CR19","doi-asserted-by":"crossref","unstructured":"Tiwari S, Gogoi M, Verma S, Singh KP (2022) Meta-learning with hopfield neural network. In: 2022 IEEE 9th Uttar Pradesh section international conference on electrical, electronics and computer engineering (UPCON), pp 1\u20135. IEEE","DOI":"10.1109\/UPCON56432.2022.9986399"},{"key":"1271_CR20","doi-asserted-by":"crossref","unstructured":"Sung F, Yang Y, Zhang L, Xiang T, Torr PHS, 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":"1271_CR21","doi-asserted-by":"crossref","unstructured":"Tang H, Li Z, Peng Z, Tang J (2020) Blockmix: meta regularization and self-calibrated inference for metric-based meta-learning. In: Proceedings of the 28th ACM international conference on multimedia, pp 610\u2013618","DOI":"10.1145\/3394171.3413884"},{"key":"1271_CR22","doi-asserted-by":"crossref","unstructured":"Peng Z, Li Z, Zhang J, Li Y, Qi GJ, Tang J (2019) Few-shot image recognition with knowledge transfer. In: Proceedings of the IEEE\/CVF international conference on computer vision, pp 441\u2013449","DOI":"10.1109\/ICCV.2019.00053"},{"key":"1271_CR23","doi-asserted-by":"crossref","unstructured":"Li Z, Tang H, Peng Z, Qi GJ, Tang J (2023) Knowledge-guided semantic transfer network for few-shot image recognition. IEEE Trans Neural Networks Learn Syst","DOI":"10.1109\/TNNLS.2023.3240195"},{"key":"1271_CR24","unstructured":"Nichol A, Schulman J (2018) Reptile: a scalable metalearning algorithm. arXiv preprint. arXiv:1803.02999"},{"key":"1271_CR25","unstructured":"Li Z, Zhou F, Chen F, Li H (2017) Meta-sgd: learning to learn quickly for few-shot learning. arXiv preprint. arXiv:1707.09835"},{"key":"1271_CR26","unstructured":"Chen WY, Liu YC, Kira Z, Wang YCF, Huang JB (2019) A closer look at few-shot classification. arXiv preprint. arXiv:1904.04232"},{"key":"1271_CR27","doi-asserted-by":"crossref","unstructured":"Tian Y, Wang Y, Krishnan D, Tenenbaum JB, 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, 2020","DOI":"10.1007\/978-3-030-58568-6_16"},{"key":"1271_CR28","doi-asserted-by":"crossref","unstructured":"Fan FL, Lai R, Wang G (2020) Quasi-equivalence of width and depth of neural networks. arXiv preprint. arXiv:2002.02515","DOI":"10.21203\/rs.3.rs-92324\/v1"},{"key":"1271_CR29","unstructured":"Nguyen T, Raghu M, Kornblith S (2020) Do wide and deep networks learn the same things? uncovering how neural network representations vary with width and depth. arXiv preprint. arXiv:2010.15327"},{"key":"1271_CR30","unstructured":"Nguyen Q, Hein M (2017) The loss surface of deep and wide neural networks. In: International conference on machine learning, pp 2603\u20132612. PMLR"},{"key":"1271_CR31","unstructured":"Lake B, Salakhutdinov R, Gross J, Tenenbaum J (2011) One shot learning of simple visual concepts. In: Proceedings of the annual meeting of the cognitive science society, vol\u00a033"},{"key":"1271_CR32","unstructured":"Oh J, Yoo H, Kim C, Yun SY (2020) Boil: towards representation change for few-shot learning. arXiv preprint. arXiv:2008.08882"},{"key":"1271_CR33","unstructured":"Miranda B, Wang YX, Koyejo S (2021) Does maml only work via feature re-use? a data centric perspective. arXiv preprint. arXiv:2112.13137"},{"key":"1271_CR34","unstructured":"Deleu T, W\u00fcrfl T, Samiei M, Cohen JP, Bengio Y (2019) Torchmeta: a meta-learning library for PyTorch. Available at: https:\/\/github.com\/tristandeleu\/pytorch-meta"},{"key":"1271_CR35","unstructured":"Arnold S, Iqbal S, Sha F (2021) When maml can adapt fast and how to assist when it cannot. In: International conference on artificial intelligence and statistics, pp 244\u2013252. PMLR"}],"container-title":["Pattern Analysis and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10044-024-01271-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10044-024-01271-2\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10044-024-01271-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,6,17]],"date-time":"2024-06-17T14:15:13Z","timestamp":1718633713000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10044-024-01271-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,5,14]]},"references-count":35,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2024,6]]}},"alternative-id":["1271"],"URL":"https:\/\/doi.org\/10.1007\/s10044-024-01271-2","relation":{},"ISSN":["1433-7541","1433-755X"],"issn-type":[{"value":"1433-7541","type":"print"},{"value":"1433-755X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,5,14]]},"assertion":[{"value":"17 September 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"15 April 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"14 May 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors have no Conflict of interest to declare to the best of their knowledge.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"63"}}