{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,4]],"date-time":"2026-05-04T10:12:14Z","timestamp":1777889534213,"version":"3.51.4"},"reference-count":63,"publisher":"IEEE","license":[{"start":{"date-parts":[[2025,10,19]],"date-time":"2025-10-19T00:00:00Z","timestamp":1760832000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2025,10,19]],"date-time":"2025-10-19T00:00:00Z","timestamp":1760832000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,10,19]]},"DOI":"10.1109\/iccv51701.2025.01722","type":"proceedings-article","created":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T19:45:49Z","timestamp":1777491949000},"page":"18531-18541","source":"Crossref","is-referenced-by-count":0,"title":["Guiding Noisy Label Conditional Diffusion Models with Score-Based Discriminator Correction"],"prefix":"10.1109","author":[{"given":"Dat Nguyen","family":"Cong","sequence":"first","affiliation":[{"name":"FPT Software AI Center"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hieu Tran","family":"Bao","sequence":"additional","affiliation":[{"name":"FPT IS AI R&#x0026;D Center"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tung","family":"Hoang-Thanh","sequence":"additional","affiliation":[{"name":"VNU University of Engineering and Technology"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1016\/0304-4149(82)90051-5"},{"key":"ref2","first-page":"1277","article-title":"From noisy prediction to true label: Noisy prediction calibration via generative model","volume-title":"International Conference on Machine Learning","author":"Bae","year":"2022"},{"key":"ref3","first-page":"24392","article-title":"Understanding and improving early stopping for learning with noisy labels","volume":"34","author":"Bai","year":"2021","journal-title":"Advances in Neural Information Processing Systems"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00175"},{"key":"ref5","article-title":"A note on the inception score","author":"Barratt","year":"2018","journal-title":"arXiv preprint arXiv"},{"key":"ref6","article-title":"Are we done with imagenet?","author":"Beyer","year":"2020","journal-title":"arXiv preprint arXiv"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-10599-4_29"},{"key":"ref8","article-title":"Denoising likelihood score matching for conditional score-based data generation","author":"Chao","year":"2022","journal-title":"ICLR"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.52202\/079017-4008"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.52202\/075280-2903"},{"key":"ref11","volume-title":"Learning with instance-dependent label noise: A sample sieve approach","author":"Cheng","year":"2021"},{"key":"ref12","article-title":"Mitigating memorization of noisy labels via regularization between representations","author":"Cheng","year":"2021","journal-title":"arXiv preprint arXiv"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"ref14","first-page":"8780","article-title":"Diffusion models beat gans on image synthesis","volume":"34","author":"Dhariwal","year":"2021","journal-title":"Advances in neural information processing systems"},{"key":"ref15","article-title":"Sharpness-aware minimization for efficiently improving generalization","author":"Foret","year":"2020","journal-title":"arXiv preprint arXiv"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.5555\/2969033.2969125"},{"key":"ref17","first-page":"297","article-title":"Noise-contrastive estimation: A new estimation principle for unnormalized statistical models","volume-title":"Proceedings of the thirteenth international conference on artificial intelligence and statistics","author":"Gutmann","year":"2010"},{"key":"ref18","article-title":"Coteaching: Robust training of deep neural networks with extremely noisy labels","author":"Han","year":"2018","journal-title":"Advances in neural information processing systems, 31"},{"key":"ref19","first-page":"4071","article-title":"Improving generalization by controlling labelnoise information in neural network weights","volume-title":"International Conference on Machine Learning","author":"Harutyunyan","year":"2020"},{"key":"ref20","article-title":"Gans trained by a two time-scale update rule converge to a local nash equilibrium","author":"Heusel","year":"2017","journal-title":"Advances in neural information processing systems, 30"},{"key":"ref21","first-page":"6840","article-title":"Denoising diffusion probabilistic models","volume":"33","author":"Ho","year":"2020","journal-title":"Advances in neural information processing systems"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00257"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.52202\/068431-1926"},{"key":"ref24","article-title":"Refining generative process with discriminator guidance in score-based diffusion models","author":"Kim","year":"2023","journal-title":"ICML"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52733.2024.02121"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00019"},{"key":"ref27","volume-title":"Learning multiple layers of features from tiny images","author":"Krizhevsky","year":"2009"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-19806-9_41"},{"key":"ref29","article-title":"Applying guidance in a limited interval improves sample and distribution quality in diffusion models","author":"Kynk\u00e4\u00e4nniemi","year":"2024","journal-title":"arXiv preprint arXiv"},{"issue":"7","key":"ref30","article-title":"Tiny imagenet visual recognition challenge","volume":"7","author":"Le","year":"2015","journal-title":"CS 231N"},{"key":"ref31","article-title":"Robust training with ensemble consensus","author":"Lee","year":"2019","journal-title":"arXiv preprint arXiv"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-20053-3_8"},{"key":"ref33","first-page":"20331","article-title":"Early-learning regularization prevents memorization of noisy labels","volume":"33","author":"Liu","year":"2020","journal-title":"Advances in neural information processing systems"},{"key":"ref34","article-title":"Labelnoise robust diffusion models","volume-title":"The Twelfth International Conference on Learning Representations","author":"Na","year":"2024"},{"key":"ref35","first-page":"7176","article-title":"Reliable fidelity and diversity metrics for generative models","volume-title":"International Conference on Machine Learning","author":"Ferjad Naeem","year":"2020"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1613\/jair.1.12125"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-75765-6_11"},{"key":"ref38","first-page":"17044","article-title":"Identifying mislabeled data using the area under the margin ranking","volume":"33","author":"Pleiss","year":"2020","journal-title":"Advances in Neural Information Processing Systems"},{"key":"ref39","first-page":"8748","article-title":"Learning transferable visual models from natural language supervision","volume-title":"International conference on machine learning","author":"Radford","year":"2021"},{"key":"ref40","article-title":"Classification accuracy score for conditional generative models","volume":"32","author":"Ravuri","year":"2019","journal-title":"Advances in neural information processing systems"},{"key":"ref41","article-title":"Improved techniques for training gans","volume-title":"Advances in Neural Information Processing Systems","author":"Salimans","year":"2016"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.52202\/068431-1833"},{"key":"ref43","first-page":"2256","article-title":"Deep unsupervised learning using nonequilibrium thermodynamics","volume-title":"Proceedings of the 32nd International Conference on Machine Learning","author":"Sohl-Dickstein","year":"2015"},{"key":"ref44","article-title":"Denoising diffusion implicit models","author":"Song","year":"2020","journal-title":"arXiv preprint arXiv"},{"key":"ref45","article-title":"Generative modeling by estimating gradients of the data distribution","author":"Song","year":"2019","journal-title":"Advances in neural information processing systems, 32"},{"key":"ref46","article-title":"Score-based generative modeling through stochastic differential equations","author":"Song","year":"2020","journal-title":"arXiv preprint arXiv"},{"key":"ref47","article-title":"Robustness of conditional gans to noisy labels","author":"Thekumparampil","year":"2018","journal-title":"Advances in neural information processing systems, 31"},{"key":"ref48","article-title":"Identifying and eliminating csam in generative ml training data and models","volume-title":"Technical report, Technical Report. Stanford University, Palo Alto, CA","author":"Thiel","year":"2023"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1162\/NECO_a_00142"},{"key":"ref50","article-title":"Diffusiondb: A large-scale prompt gallery dataset for text-toimage generative models","author":"Wang","year":"2022","journal-title":"arXiv preprint arXiv"},{"key":"ref51","article-title":"Robust early-learning: Hindering the memorization of noisy labels","volume-title":"International conference on learning representations","author":"Xia","year":"2020"},{"key":"ref52","first-page":"7597","article-title":"Part-dependent label noise: Towards instance-dependent label noise","volume":"33","author":"Xia","year":"2020","journal-title":"Advances in Neural Information Processing Systems"},{"key":"ref53","article-title":"Sample selection with uncertainty of losses for learning with noisy labels","author":"Xia","year":"2021","journal-title":"arXiv preprint arXiv"},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7298885"},{"key":"ref55","first-page":"7260","article-title":"Dual t: Reducing estimation error for transition matrix in label-noise learning","volume":"33","author":"Yao","year":"2020","journal-title":"Advances in neural information processing systems"},{"key":"ref56","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.01618"},{"key":"ref57","first-page":"7164","article-title":"How does disagreement help generalization against label corruption?","volume-title":"International conference on machine learning","author":"Yu","year":"2019"},{"key":"ref58","doi-asserted-by":"publisher","DOI":"10.1145\/3446776"},{"key":"ref59","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4899-7687-1_79"},{"key":"ref60","first-page":"12501","article-title":"Learning noise transition matrix from only noisy labels via total variation regularization","volume-title":"International Conference on Machine Learning","author":"Zhang","year":"2021"},{"key":"ref61","first-page":"7559","article-title":"Differentiable augmentation for data-efficient gan training","volume":"33","author":"Zhao","year":"2020","journal-title":"Advances in neural information processing systems"},{"key":"ref62","article-title":"Robust curriculum learning: from clean label detection to noisy label self-correction","volume-title":"International Conference on Learning Representations","author":"Zhou","year":"2020"},{"key":"ref63","first-page":"27412","article-title":"Detecting corrupted labels without training a model to predict","volume-title":"International conference on machine learning","author":"Zhu","year":"2022"}],"event":{"name":"2025 IEEE\/CVF International Conference on Computer Vision (ICCV)","location":"Honolulu, HI, USA","start":{"date-parts":[[2025,10,19]]},"end":{"date-parts":[[2025,10,25]]}},"container-title":["2025 IEEE\/CVF International Conference on Computer Vision (ICCV)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/11443115\/11443287\/11444483.pdf?arnumber=11444483","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T05:11:56Z","timestamp":1777612316000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/11444483\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,19]]},"references-count":63,"URL":"https:\/\/doi.org\/10.1109\/iccv51701.2025.01722","relation":{},"subject":[],"published":{"date-parts":[[2025,10,19]]}}}