{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,27]],"date-time":"2025-09-27T13:51:12Z","timestamp":1758981072068,"version":"3.37.3"},"reference-count":42,"publisher":"Springer Science and Business Media LLC","issue":"20","license":[{"start":{"date-parts":[[2023,7,25]],"date-time":"2023-07-25T00:00:00Z","timestamp":1690243200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,7,25]],"date-time":"2023-07-25T00:00:00Z","timestamp":1690243200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/100014718","name":"Innovative Research Group Project of the National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["No. 61402537"],"award-info":[{"award-number":["No. 61402537"]}],"id":[{"id":"10.13039\/100014718","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100012542","name":"Sichuan Province Science and Technology Support Program","doi-asserted-by":"publisher","award":["Nos. 2019ZDZX0006"],"award-info":[{"award-number":["Nos. 2019ZDZX0006"]}],"id":[{"id":"10.13039\/100012542","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Appl Intell"],"published-print":{"date-parts":[[2023,10]]},"DOI":"10.1007\/s10489-023-04609-1","type":"journal-article","created":{"date-parts":[[2023,7,25]],"date-time":"2023-07-25T15:30:05Z","timestamp":1690299005000},"page":"24393-24406","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Learning with noisy labels via logit adjustment based on gradient prior method"],"prefix":"10.1007","volume":"53","author":[{"given":"Boyi","family":"Fu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuncong","family":"Peng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaolin","family":"Qin","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,7,25]]},"reference":[{"doi-asserted-by":"crossref","unstructured":"Zou Z, Chen K, Shi Z, Guo Y, Ye J (2023) Object detection in 20 years: a survey. Proceedings of the IEEE","key":"4609_CR1","DOI":"10.1109\/JPROC.2023.3238524"},{"doi-asserted-by":"crossref","unstructured":"Peng Y, Fu B, Qin X (2022) Meta-style: few-shot learning dataset for social media field. In: International conference on artificial neural networks. Springer, pp 433\u2013444","key":"4609_CR2","DOI":"10.1007\/978-3-031-15937-4_36"},{"doi-asserted-by":"crossref","unstructured":"Zhuang F, Qi Z, Duan K, Xi D, Zhu Y, Zhu H, Xiong H, He Q (2020) A comprehensive survey on transfer learning. Proc IEEE 109(1):43\u201376","key":"4609_CR3","DOI":"10.1109\/JPROC.2020.3004555"},{"doi-asserted-by":"crossref","unstructured":"Northcutt C, Jiang L, Chuang I (2021) Confident learning: estimating uncertainty in dataset labels. J Artif Intell Res 70:1373\u20131411","key":"4609_CR4","DOI":"10.1613\/jair.1.12125"},{"doi-asserted-by":"crossref","unstructured":"Garg A, Nguyen C, Felix R, Do T-T, Carneiro G (2023) Instance-dependent noisy label learning via graphical modelling. In: Proceedings of the IEEE\/CVF winter conference on applications of computer vision. pp 2288\u20132298","key":"4609_CR5","DOI":"10.1109\/WACV56688.2023.00232"},{"unstructured":"Vahdat A (2017) Toward robustness against label noise in training deep discriminative neural networks. Adv Neural Inf Proces Syst 30","key":"4609_CR6"},{"doi-asserted-by":"crossref","unstructured":"Lee K-H, He X, Zhang L, Yang L (2018) Cleannet: transfer learning for scalable image classifier training with label noise. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp 5447\u20135456","key":"4609_CR7","DOI":"10.1109\/CVPR.2018.00571"},{"doi-asserted-by":"crossref","unstructured":"Li Y, Yang J, Song Y, Cao L, Luo J, Li L-J (2017) Learning from noisy labels with distillation. In: Proceedings of the IEEE international conference on computer vision. pp 1910\u20131918","key":"4609_CR8","DOI":"10.1109\/ICCV.2017.211"},{"doi-asserted-by":"crossref","unstructured":"Patrini G, Rozza A, Menon AK, Nock R, Qu L (2017) Making deep neural networks robust to label noise: a loss correction approach. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp 1944\u20131952","key":"4609_CR9","DOI":"10.1109\/CVPR.2017.240"},{"unstructured":"Han B, Yao J, Niu G, Zhou M, Tsang I, Zhang Y, Sugiyama M (2018) Masking: a new perspective of noisy supervision. Adv Neural Inf Proces Syst 31","key":"4609_CR10"},{"unstructured":"Sukhbaatar S, Bruna J, Paluri M, Bourdev L, Fergus R (2015) Training convolutional networks with noisy labels. In: 3rd International conference on learning representations","key":"4609_CR11"},{"unstructured":"Goldberger J, Ben-Reuven E (2017) Training deep neural-networks using a noise adaptation layer. In: International conference on learning representations","key":"4609_CR12"},{"issue":"4","key":"4609_CR13","doi-asserted-by":"publisher","first-page":"1909","DOI":"10.1109\/TIP.2018.2877939","volume":"28","author":"J Yao","year":"2018","unstructured":"Yao J, Wang J, Tsang IW, Zhang Y, Sun J, Zhang C, Zhang R (2018) Deep learning from noisy image labels with quality embedding. IEEE Trans Image Process 28(4):1909\u20131922","journal-title":"IEEE Trans Image Process"},{"unstructured":"Jiang L, Zhou Z, Leung T, Li L-J, Fei-Fei L (2018) Mentornet: learning data-driven curriculum for very deep neural networks on corrupted labels. In: International conference on machine learning. PMLR, pp 2304\u20132313","key":"4609_CR14"},{"unstructured":"Yu X, Han B, Yao J, Niu G, Tsang I, Sugiyama M (2019) How does disagreement help generalization against label corruption? In: International conference on machine learning. PMLR, pp 7164\u20137173","key":"4609_CR15"},{"unstructured":"Malach E, Shalev-Shwartz S (2017) Decoupling \u201cwhen to update\u201d from \u201chow to update\u201d. Adv Neural Inf Proces Syst 30","key":"4609_CR16"},{"unstructured":"Han B, Yao Q, Yu X, Niu G, Xu M, Hu W, Tsang I, Sugiyama M (2018) Co-teaching: robust training of deep neural networks with extremely noisy labels. Adv Neural Inf Proces Syst 31","key":"4609_CR17"},{"doi-asserted-by":"crossref","unstructured":"Wang Y, Ma X, Chen Z, Luo Y, Yi J, Bailey J (2019) Symmetric cross entropy for robust learning with noisy labels. In: Proceedings of the IEEE\/CVF international conference on computer vision. pp 322\u2013330","key":"4609_CR18","DOI":"10.1109\/ICCV.2019.00041"},{"unstructured":"Wang X, Kodirov E, Hua Y, Robertson NM. IMAE for noise-robust learning: mean absolute error does not treat examples equally and gradient magnitude\u2019s variance matters","key":"4609_CR19"},{"doi-asserted-by":"crossref","unstructured":"Lin T-Y, Goyal P, Girshick R, He K, Doll\u00e1r P (2017) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision. pp 2980\u20132988","key":"4609_CR20","DOI":"10.1109\/ICCV.2017.324"},{"unstructured":"Zhang Z, Sabuncu M (2018) Generalized cross entropy loss for training deep neural networks with noisy labels. Adv Neural Inf Proces Syst 31","key":"4609_CR21"},{"doi-asserted-by":"crossref","unstructured":"Feng L, Shu S, Lin Z, Lv F, Li L, An B (2021) Can cross entropy loss be robust to label noise? In: Proceedings of the twenty-ninth international conference on international joint conferences on artificial intelligence. pp 2206\u20132212","key":"4609_CR22","DOI":"10.24963\/ijcai.2020\/305"},{"unstructured":"Amid E, Warmuth MKK, Anil R, Koren T (2019) Robust bi-tempered logistic loss based on Bregman divergences. Adv Neural Inf Proces Syst 32","key":"4609_CR23"},{"unstructured":"Ma X, Huang H, Wang Y, Romano S, Erfani S, Bailey J (2020) Normalized loss functions for deep learning with noisy labels. In: International conference on machine learning. PMLR, pp 6543\u20136553","key":"4609_CR24"},{"doi-asserted-by":"crossref","unstructured":"Ghosh A, Kumar H, Sastry PS (2017) Robust loss functions under label noise for deep neural networks. In: Proceedings of the AAAI conference on artificial intelligence, vol\u00a031","key":"4609_CR25","DOI":"10.1609\/aaai.v31i1.10894"},{"unstructured":"Menon AK, Jayasumana S, Rawat AS, Jain H, Veit A, Kumar S. Long-tail learning via logit adjustment. In: International conference on learning representations","key":"4609_CR26"},{"issue":"1","key":"4609_CR27","doi-asserted-by":"publisher","first-page":"168","DOI":"10.1016\/j.aci.2018.08.003","volume":"17","author":"A Tharwat","year":"2021","unstructured":"Tharwat A (2021) Classification assessment methods. Appl Comput Inform 17(1):168\u2013192","journal-title":"Appl Comput Inform"},{"unstructured":"M\u00fcller R, Kornblith S, Hinton GE (2019) When does label smoothing help? Adv Neural Inf Proces Syst 32","key":"4609_CR28"},{"doi-asserted-by":"crossref","unstructured":"Samuel Dvir, Chechik Gal (2021) Distributional robustness loss for long-tail learning. In: Proceedings of the IEEE\/CVF international conference on computer vision. pp 9495\u20139504","key":"4609_CR29","DOI":"10.1109\/ICCV48922.2021.00936"},{"issue":"11","key":"4609_CR30","doi-asserted-by":"publisher","first-page":"2278","DOI":"10.1109\/5.726791","volume":"86","author":"Y LeCun","year":"1998","unstructured":"LeCun Y, Bottou L, Bengio Y, Haffner P (1998) Gradient-based learning applied to document recognition. Proc IEEE 86(11):2278\u20132324","journal-title":"Proc IEEE"},{"unstructured":"Krizhevsky A, Hinton G et\u00a0al (2009) Learning multiple layers of features from tiny images","key":"4609_CR31"},{"unstructured":"Li W, Wang L, Li W, Agustsson E, Gool LV (2017) WebVision database: visual learning and understanding from web data. CoRR","key":"4609_CR32"},{"issue":"2","key":"4609_CR33","doi-asserted-by":"publisher","first-page":"303","DOI":"10.1007\/s11263-009-0275-4","volume":"88","author":"M Everingham","year":"2010","unstructured":"Everingham M, Van Gool L, Williams CKI, Winn J, Zisserman A (2010) The Pascal visual object classes (VOC) challenge. Int J Comput Vis 88(2):303\u2013338","journal-title":"Int J Comput Vis"},{"issue":"1","key":"4609_CR34","doi-asserted-by":"publisher","first-page":"98","DOI":"10.1007\/s11263-014-0733-5","volume":"111","author":"SM Mark Everingham","year":"2015","unstructured":"Mark Everingham SM, Van Eslami Luc, Gool Christopher KI, Winn Williams John, Andrew Zisserman (2015) The pascal visual object classes challenge: a retrospective. Int J Comput Vis 111(1):98\u2013136","journal-title":"Int J Comput Vis"},{"doi-asserted-by":"crossref","unstructured":"Gavrikov P, Keuper J (2022) CNN filter DB: an empirical investigation of trained convolutional filters. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition. pp 19066\u201319076","key":"4609_CR35","DOI":"10.1109\/CVPR52688.2022.01848"},{"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","key":"4609_CR36","DOI":"10.1109\/CVPR.2016.90"},{"doi-asserted-by":"crossref","unstructured":"Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D, Erhan D, Vanhoucke V, Rabinovich A (2015) Going deeper with convolutions. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp 1\u20139","key":"4609_CR37","DOI":"10.1109\/CVPR.2015.7298594"},{"unstructured":"Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition","key":"4609_CR38"},{"doi-asserted-by":"crossref","unstructured":"Szegedy C, Vanhoucke V, Ioffe S, Shlens J, Wojna Z (2016) Rethinking the inception architecture for computer vision. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp 2818\u20132826","key":"4609_CR39","DOI":"10.1109\/CVPR.2016.308"},{"unstructured":"Howard AG, Zhu M, Chen B, Kalenichenko D, Wang W, Weyand T, Andreetto M, Adam H. Mobilenets: efficient convolutional neural networks for mobile vision applications","key":"4609_CR40"},{"doi-asserted-by":"crossref","unstructured":"Deng J, Dong W, Socher R, Li L-J, Li K, Fei-Fei L (2009) Imagenet: a large-scale hierarchical image database. In: 2009 IEEE conference on computer vision and pattern recognition. IEEE, pp 248\u2013255","key":"4609_CR41","DOI":"10.1109\/CVPR.2009.5206848"},{"unstructured":"Ren S, He K, Girshick R, Sun J (2015) Faster R-CNN: towards real-time object detection with region proposal networks. Adv Neural Inf Proces Syst 28","key":"4609_CR42"}],"container-title":["Applied Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-023-04609-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10489-023-04609-1\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-023-04609-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,10,21]],"date-time":"2023-10-21T16:25:49Z","timestamp":1697905549000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10489-023-04609-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,7,25]]},"references-count":42,"journal-issue":{"issue":"20","published-print":{"date-parts":[[2023,10]]}},"alternative-id":["4609"],"URL":"https:\/\/doi.org\/10.1007\/s10489-023-04609-1","relation":{},"ISSN":["0924-669X","1573-7497"],"issn-type":[{"type":"print","value":"0924-669X"},{"type":"electronic","value":"1573-7497"}],"subject":[],"published":{"date-parts":[[2023,7,25]]},"assertion":[{"value":"3 April 2023","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"25 July 2023","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 data used is completely public and free, and does not involve ethics and informed consent.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical and informed consent for data used"}},{"value":"The authors declare there is no conflict of interest.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}