{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,26]],"date-time":"2026-02-26T15:22:54Z","timestamp":1772119374087,"version":"3.50.1"},"reference-count":59,"publisher":"Springer Science and Business Media LLC","issue":"7","license":[{"start":{"date-parts":[[2024,5,30]],"date-time":"2024-05-30T00:00:00Z","timestamp":1717027200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,5,30]],"date-time":"2024-05-30T00:00:00Z","timestamp":1717027200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62376173"],"award-info":[{"award-number":["62376173"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Knowl Inf Syst"],"published-print":{"date-parts":[[2024,7]]},"DOI":"10.1007\/s10115-024-02141-3","type":"journal-article","created":{"date-parts":[[2024,5,30]],"date-time":"2024-05-30T14:01:32Z","timestamp":1717077692000},"page":"3855-3881","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["PatchMix: patch-level mixup for data augmentation in convolutional neural networks"],"prefix":"10.1007","volume":"66","author":[{"given":"Yichao","family":"Hong","sequence":"first","affiliation":[]},{"given":"Yuanyuan","family":"Chen","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,5,30]]},"reference":[{"issue":"6","key":"2141_CR1","doi-asserted-by":"publisher","first-page":"84","DOI":"10.1145\/3065386","volume":"60","author":"A Krizhevsky","year":"2017","unstructured":"Krizhevsky A, Sutskever I, Hinton GE (2017) Imagenet classification with deep convolutional neural networks. Commun ACM 60(6):84\u201390","journal-title":"Commun ACM"},{"issue":"7","key":"2141_CR2","doi-asserted-by":"publisher","first-page":"2805","DOI":"10.1007\/s10115-023-01853-2","volume":"65","author":"Z Yang","year":"2023","unstructured":"Yang Z, Sinnott RO, Bailey J, Ke Q (2023) A survey of automated data augmentation algorithms for deep learning-based image classification tasks. Knowl Inf Syst 65(7):2805\u20132861","journal-title":"Knowl Inf Syst"},{"key":"2141_CR3","doi-asserted-by":"crossref","unstructured":"Ammar S, Bouwmans T, Zaghden N, Neji M (2020) Towards an effective approach for face recognition with DCGANs data augmentation. In: Advances in visual computing: 15th International symposium, ISVC 2020, San Diego, USA, October 5\u20137, 2020, proceedings, part I 15, pp 463\u2013475","DOI":"10.1007\/978-3-030-64556-4_36"},{"key":"2141_CR4","doi-asserted-by":"crossref","unstructured":"Bae G, La\u00a0Gorce M, Baltru\u0161aitis T, Hewitt C, Chen D, Valentin J, Cipolla R, Shen J (2023) Digiface-1m: 1 million digital face images for face recognition. In: Proceedings of the IEEE\/CVF winter conference on applications of computer vision, pp 3526\u20133535","DOI":"10.1109\/WACV56688.2023.00352"},{"key":"2141_CR5","doi-asserted-by":"crossref","unstructured":"Sakkos D, Shum HP, Ho ES (2019) Illumination-based data augmentation for robust background subtraction. In: 2019 13th international conference on software, knowledge, information management and applications (SKIMA). Island of Ulkulhas, Maldives, pp 1\u20138","DOI":"10.1109\/SKIMA47702.2019.8982527"},{"issue":"3","key":"2141_CR6","doi-asserted-by":"publisher","first-page":"93","DOI":"10.3390\/fi14030093","volume":"14","author":"N Cauli","year":"2022","unstructured":"Cauli N, Reforgiato Recupero D (2022) Survey on videos data augmentation for deep learning models. Future Internet 14(3):93","journal-title":"Future Internet"},{"issue":"4","key":"2141_CR7","doi-asserted-by":"publisher","first-page":"1713","DOI":"10.1007\/s10115-022-01815-0","volume":"65","author":"L Silva","year":"2023","unstructured":"Silva L, Barbosa L (2023) Matching news articles and wikipedia tables for news augmentation. Knowl Inf Syst 65(4):1713\u20131734","journal-title":"Knowl Inf Syst"},{"issue":"4","key":"2141_CR8","doi-asserted-by":"publisher","first-page":"1393","DOI":"10.1007\/s10115-019-01392-9","volume":"62","author":"V Iosifidis","year":"2020","unstructured":"Iosifidis V, Ntoutsi E (2020) Sentiment analysis on big sparse data streams with limited labels. Knowl Inf Syst 62(4):1393\u20131432","journal-title":"Knowl Inf Syst"},{"issue":"3","key":"2141_CR9","doi-asserted-by":"publisher","first-page":"107","DOI":"10.1145\/3446776","volume":"64","author":"C Zhang","year":"2021","unstructured":"Zhang C, Bengio S, Hardt M, Recht B, Vinyals O (2021) Understanding deep learning (still) requires rethinking generalization. Commun ACM 64(3):107\u2013115","journal-title":"Commun ACM"},{"key":"2141_CR10","unstructured":"Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556"},{"key":"2141_CR11","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 770\u2013778","DOI":"10.1109\/CVPR.2016.90"},{"key":"2141_CR12","doi-asserted-by":"crossref","unstructured":"M\u00fcller SG, Hutter F (2021) Trivialaugment: tuning-free yet state-of-the-art data augmentation. In: Proceedings of the IEEE\/CVF international conference on computer vision, pp 774\u2013782","DOI":"10.1109\/ICCV48922.2021.00081"},{"key":"2141_CR13","unstructured":"DeVries T, Taylor GW (2017) Improved regularization of convolutional neural networks with cutout. arXiv preprint arXiv:1708.04552"},{"key":"2141_CR14","unstructured":"Lopes RG, Yin D, Poole B, Gilmer J, Cubuk ED (2019) Improving robustness without sacrificing accuracy with patch gaussian augmentation. arXiv preprint arXiv:1906.02611"},{"key":"2141_CR15","doi-asserted-by":"crossref","unstructured":"Zhong Z, Zheng L, Kang G, Li S, Yang Y (2020) Random erasing data augmentation. In: Proceedings of the AAAI conference on artificial intelligence, vol 34, pp 13001\u201313008","DOI":"10.1609\/aaai.v34i07.7000"},{"key":"2141_CR16","unstructured":"Dosovitskiy A, Beyer L, Kolesnikov A, Weissenborn D, Zhai X, Unterthiner T, Dehghani M, Minderer M, Heigold G, Gelly S, Uszkoreit J, Houlsby N (2021) An image is worth 16$$\\times $$16 words: transformers for image recognition at scale. In: International conference on learning representations, online"},{"key":"2141_CR17","unstructured":"Trockman A, Kolter JZ (2022) Patches are all you need? arXiv preprint arXiv:2201.09792"},{"key":"2141_CR18","doi-asserted-by":"crossref","unstructured":"Wei C, Xie L, Ren X, Xia Y, Su C, Liu J, Tian Q, Yuille AL (2019) Iterative reorganization with weak spatial constraints: solving arbitrary jigsaw puzzles for unsupervised representation learning. In Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 1910\u20131919","DOI":"10.1109\/CVPR.2019.00201"},{"key":"2141_CR19","unstructured":"Zhang H, Cisse M, Dauphin YN, Lopez-Paz D (2018) Mixup: beyond empirical risk minimization. In: International conference on learning representations. Vancouver, Canada"},{"key":"2141_CR20","unstructured":"Verma V, Lamb A, Beckham C, Najafi A, Mitliagkas I, Lopez-Paz D, Bengio Y (2019) Manifold mixup: better representations by interpolating hidden states. In: International conference on machine learning, pp 6438\u20136447"},{"key":"2141_CR21","doi-asserted-by":"crossref","unstructured":"Navarro M, Little C, Allen GI, Segarra S (2024) Data augmentation via subgroup mixup for improving fairness. In ICASSP 2024-2024 IEEE international conference on acoustics, speech and signal processing (ICASSP), pp 7350\u20137354","DOI":"10.1109\/ICASSP48485.2024.10446564"},{"key":"2141_CR22","doi-asserted-by":"crossref","unstructured":"Yun S, Han D, Oh SJ, Chun S, Choe J, Yoo Y (2019) Cutmix: regularization strategy to train strong classifiers with localizable features. In: Proceedings of the IEEE\/CVF International conference on computer vision, pp 6023\u20136032","DOI":"10.1109\/ICCV.2019.00612"},{"key":"2141_CR23","unstructured":"Kim J-H, Choo W, Song HO (2020) Puzzle mix: exploiting saliency and local statistics for optimal mixup. In: International conference on machine learning, pp 5275\u20135285"},{"key":"2141_CR24","unstructured":"Uddin AFMS, Monira MS, Shin W, Chung T, Bae S-H (2021) Saliencymix: a saliency guided data augmentation strategy for better regularization. In: International conference on learning representations, online"},{"key":"2141_CR25","doi-asserted-by":"crossref","unstructured":"Liu Z, Li S, Wu D, Liu Z, Chen Z, Wu L, Li, SZ (2022) Automix: unveiling the power of mixup for stronger classifiers. In: Computer vision\u2013ECCV 2022: 17th European conference, Tel Aviv, Israel, October 23\u201327, 2022, proceedings, part XXIV, pp 441\u2013458","DOI":"10.1007\/978-3-031-20053-3_26"},{"key":"2141_CR26","unstructured":"Schneider N, Goshtasbpour S, Perez-Cruz F (2023) Anchor data augmentation. In: Thirty-seventh Conference on neural information processing systems"},{"key":"2141_CR27","first-page":"3361","volume":"35","author":"H Yao","year":"2022","unstructured":"Yao H, Wang Y, Zhang L, Zou JY, Finn C (2022) C-mixup: improving generalization in regression. Adv Neural Inf Process Syst 35:3361\u20133376","journal-title":"Adv Neural Inf Process Syst"},{"key":"2141_CR28","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":"2141_CR29","unstructured":"Kang G, Dong X, Zheng L, Yang Y (2017) Patchshuffle regularization. arXiv preprint arXiv:1707.07103"},{"issue":"1","key":"2141_CR30","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s40537-019-0197-0","volume":"6","author":"C Shorten","year":"2019","unstructured":"Shorten C, Khoshgoftaar TM (2019) A survey on image data augmentation for deep learning. J Big Data 6(1):1\u201348","journal-title":"J Big Data"},{"key":"2141_CR31","doi-asserted-by":"crossref","unstructured":"Cubuk ED, Zoph B, Mane D, Vasudevan V, Le QV (2019) Autoaugment: learning augmentation strategies from data. In Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 113\u2013123","DOI":"10.1109\/CVPR.2019.00020"},{"key":"2141_CR32","doi-asserted-by":"crossref","unstructured":"Cubuk ED, Zoph B, Shlens J, Le QV (2020) Randaugment: practical automated data augmentation with a reduced search space. In Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition workshops, pp 702\u2013703","DOI":"10.1109\/CVPRW50498.2020.00359"},{"key":"2141_CR33","unstructured":"Kim J, Choo W, Jeong H, Song HO (2021) Co-mixup: saliency guided joint mixup with supermodular diversity. In: International conference on learning representations, Online"},{"key":"2141_CR34","doi-asserted-by":"crossref","unstructured":"Venkataramanan S, Kijak E, Amsaleg L, Avrithis Y (2022) Alignmixup: improving representations by interpolating aligned features. In Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 19174\u201319183","DOI":"10.1109\/CVPR52688.2022.01858"},{"issue":"9","key":"2141_CR35","doi-asserted-by":"publisher","first-page":"759","DOI":"10.1016\/S0262-8856(03)00069-6","volume":"21","author":"Y-C Cheng","year":"2003","unstructured":"Cheng Y-C, Chen S-Y (2003) Image classification using color, texture and regions. Image Vis Comput 21(9):759\u2013776","journal-title":"Image Vis Comput"},{"key":"2141_CR36","unstructured":"Lee K, Lee K, Shin J, Lee H (2019) Network randomization: a simple technique for generalization in deep reinforcement learning. arXiv preprint arXiv:1910.05396"},{"key":"2141_CR37","doi-asserted-by":"publisher","first-page":"119838","DOI":"10.1016\/j.ins.2023.119838","volume":"654","author":"H Eghbal-zadeh","year":"2024","unstructured":"Eghbal-zadeh H, Zellinger W, Pintor M, Grosse K, Koutini K, Moser BA, Biggio B, Widmer G (2024) Rethinking data augmentation for adversarial robustness. Inf Sci 654:119838","journal-title":"Inf Sci"},{"key":"2141_CR38","unstructured":"Krizhevsky A, Hinton G (2009) Learning multiple layers of features from tiny images. Technical report, University of Toronto"},{"key":"2141_CR39","unstructured":"Chrabaszcz P, Loshchilov I, Hutter F (2017) A downsampled variant of imagenet as an alternative to the cifar datasets. arXiv preprint arXiv:1707.08819"},{"key":"2141_CR40","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J (2016) Identity mappings in deep residual networks. In: Computer vision\u2013ECCV 2016: 14th European conference, Amsterdam, The Netherlands, October 11\u201314, 2016, proceedings, part IV 14, pp 630\u2013645","DOI":"10.1007\/978-3-319-46493-0_38"},{"key":"2141_CR41","doi-asserted-by":"crossref","unstructured":"Zagoruyko S, Komodakis N (2016) Wide residual networks. arXiv preprint arXiv:1605.07146","DOI":"10.5244\/C.30.87"},{"key":"2141_CR42","doi-asserted-by":"crossref","unstructured":"Huang G, Liu Z, Van Der\u00a0Maaten L, Weinberger KQ (2017) Densely connected convolutional networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4700\u20134708","DOI":"10.1109\/CVPR.2017.243"},{"key":"2141_CR43","doi-asserted-by":"crossref","unstructured":"Sandler M, Howard A, Zhu M, Zhmoginov A, Chen L-C (2018) Mobilenetv2: inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510\u20134520","DOI":"10.1109\/CVPR.2018.00474"},{"key":"2141_CR44","doi-asserted-by":"publisher","DOI":"10.1109\/TAI.2022.3185179","author":"HD Kabir","year":"2022","unstructured":"Kabir HD, Abdar M, Khosravi A, Jalali SMJ, Atiya AF, Nahavandi S, Srinivasan D (2022) Spinalnet: deep neural network with gradual input. IEEE Trans Artif Intell. https:\/\/doi.org\/10.1109\/TAI.2022.3185179","journal-title":"IEEE Trans Artif Intell"},{"key":"2141_CR45","doi-asserted-by":"publisher","unstructured":"Li F-F, Andreeto M, Ranzato M, Perona P (2022) Caltech 101. CaltechDATA. https:\/\/doi.org\/10.22002\/D1.20086","DOI":"10.22002\/D1.20086"},{"key":"2141_CR46","doi-asserted-by":"crossref","unstructured":"Cimpoi M, Maji S, Kokkinos I, Mohamed S, Vedaldi A (2014) Describing textures in the wild. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 3606\u20133613","DOI":"10.1109\/CVPR.2014.461"},{"key":"2141_CR47","doi-asserted-by":"crossref","unstructured":"Krause J, Stark M, Deng J, Fei-Fei L (2013) 3d object representations for fine-grained categorization. In: Proceedings of the IEEE international conference on computer vision workshops, pp 554\u2013561","DOI":"10.1109\/ICCVW.2013.77"},{"key":"2141_CR48","doi-asserted-by":"crossref","unstructured":"Nilsback M-E, Zisserman A (2008) Automated flower classification over a large number of classes. In: 2008 Sixth Indian conference on computer vision, graphics & image processing, pp 722\u2013729","DOI":"10.1109\/ICVGIP.2008.47"},{"key":"2141_CR49","doi-asserted-by":"crossref","unstructured":"Bromley J, Guyon I, LeCun Y, S\u00e4ckinger E, Shah R (1993) Signature verification using a \u201cSiamese\u201d time delay neural network. In: Proceedings of the 6th international conference on neural information processing systems. Morgan Kaufmann Publishers Inc., San Francisco, pp 737\u2013744","DOI":"10.1142\/S0218001493000339"},{"key":"2141_CR50","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"},{"key":"2141_CR51","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"},{"issue":"1","key":"2141_CR52","doi-asserted-by":"publisher","first-page":"21","DOI":"10.1109\/TIT.1967.1053964","volume":"13","author":"T Cover","year":"1967","unstructured":"Cover T, Hart P (1967) Nearest neighbor pattern classification. IEEE Trans Inf Theory 13(1):21\u201327","journal-title":"IEEE Trans Inf Theory"},{"key":"2141_CR53","unstructured":"Hendrycks D, Dietterich T (2019) Benchmarking neural network robustness to common corruptions and perturbations. arXiv preprint arXiv:1903.12261"},{"key":"2141_CR54","doi-asserted-by":"crossref","unstructured":"Zhou B, Khosla A, Lapedriza A, Oliva A, Torralba A (2016) Learning deep features for discriminative localization. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2921\u20132929","DOI":"10.1109\/CVPR.2016.319"},{"key":"2141_CR55","unstructured":"Dosovitskiy A, Beyer L, Kolesnikov A, Weissenborn D, Zhai X, Unterthiner T, Dehghani M, Minderer M, Heigold G, Gelly S et al (2020) An image is worth 16$$\\times $$16 words: transformers for image recognition at scale. arXiv preprint arXiv:2010.11929"},{"key":"2141_CR56","unstructured":"Cascante-Bonilla P, Sekhon A, Qi Y, Ordonez V (2021) Evolving image compositions for feature representation learning. arXiv preprint arXiv:2106.09011"},{"key":"2141_CR57","doi-asserted-by":"crossref","unstructured":"Xu J, Xie H, Xu H, Wang Y, Liu S-A, Zhang Y (2022) Boat in the sky: background decoupling and object-aware pooling for weakly supervised semantic segmentation. In: Proceedings of the 30th ACM international conference on multimedia, pp 5783\u20135792","DOI":"10.1145\/3503161.3548201"},{"key":"2141_CR58","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2023.3309621","author":"L Zhu","year":"2023","unstructured":"Zhu L, She Q, Chen Q, Meng X, Geng M, Jin L, Zhang Y, Ren Q, Lu Y (2023) Background-aware classification activation map for weakly supervised object localization. IEEE Trans Pattern Anal Mach Intell. https:\/\/doi.org\/10.1109\/TPAMI.2023.3309621","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"2141_CR59","doi-asserted-by":"crossref","unstructured":"Zhu J, Bai H, Wang L (2023) Patch-mix transformer for unsupervised domain adaptation: a game perspective. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 3561\u20133571","DOI":"10.1109\/CVPR52729.2023.00347"}],"container-title":["Knowledge and Information Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10115-024-02141-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10115-024-02141-3\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10115-024-02141-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,6,21]],"date-time":"2024-06-21T03:16:57Z","timestamp":1718939817000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10115-024-02141-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,5,30]]},"references-count":59,"journal-issue":{"issue":"7","published-print":{"date-parts":[[2024,7]]}},"alternative-id":["2141"],"URL":"https:\/\/doi.org\/10.1007\/s10115-024-02141-3","relation":{"has-preprint":[{"id-type":"doi","id":"10.21203\/rs.3.rs-3612931\/v1","asserted-by":"object"}]},"ISSN":["0219-1377","0219-3116"],"issn-type":[{"value":"0219-1377","type":"print"},{"value":"0219-3116","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,5,30]]},"assertion":[{"value":"15 November 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"6 May 2024","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"14 May 2024","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"30 May 2024","order":4,"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 conflict of interest to this work.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}