{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,7]],"date-time":"2026-04-07T16:28:38Z","timestamp":1775579318333,"version":"3.50.1"},"reference-count":58,"publisher":"Springer Science and Business Media LLC","issue":"11","license":[{"start":{"date-parts":[[2025,8,8]],"date-time":"2025-08-08T00:00:00Z","timestamp":1754611200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,8,8]],"date-time":"2025-08-08T00:00:00Z","timestamp":1754611200000},"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":["Int J Comput Vis"],"published-print":{"date-parts":[[2025,11]]},"DOI":"10.1007\/s11263-025-02536-x","type":"journal-article","created":{"date-parts":[[2025,8,8]],"date-time":"2025-08-08T13:57:05Z","timestamp":1754661425000},"page":"7710-7725","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Sample-Aware RandAugment: Search-Free Automatic Data Augmentation for Effective Image Recognition"],"prefix":"10.1007","volume":"133","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7356-0434","authenticated-orcid":false,"given":"Anqi","family":"Xiao","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0003-7935-2358","authenticated-orcid":false,"given":"Weichen","family":"Yu","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4208-1200","authenticated-orcid":false,"given":"Hongyuan","family":"Yu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,8,8]]},"reference":[{"key":"2536_CR1","doi-asserted-by":"crossref","unstructured":"Bekor T, Nayman N, Zelnik-Manor L (2024) Freeaugment: Data augmentation search across all degrees of freedom. In European Conference on Computer Vision (ECCV)","DOI":"10.1007\/978-3-031-73383-3_3"},{"key":"2536_CR2","doi-asserted-by":"crossref","unstructured":"Bossard L, Guillaumin M, Van\u00a0Gool L (2014) Food-101\u2013mining discriminative components with random forests. In Computer Vision\u2013ECCV 2014: 13th European Conference, Zurich, Switzerland, September 6-12, 2014, Proceedings, Part VI 13, Springer, pp.\u00a0446\u2013461","DOI":"10.1007\/978-3-319-10599-4_29"},{"key":"2536_CR3","unstructured":"Cheung TH, Yeung DY (2021) Adaaug: Learning class-and instance-adaptive data augmentation policies. In International Conference on Learning Representations"},{"key":"2536_CR4","doi-asserted-by":"crossref","unstructured":"Cubuk ED, Zoph B, Man\u00e9 D, Vasudevan, V., & Le, Q. V. (2019) Autoaugment: Learning augmentation strategies from data. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR) pp.\u00a0113\u2013123","DOI":"10.1109\/CVPR.2019.00020"},{"key":"2536_CR5","doi-asserted-by":"crossref","unstructured":"Cubuk ED, Zoph B, Shlens J, & Le, Q. V. (2020) Randaugment: Practical automated data augmentation with a reduced search space. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR) pp.\u00a03008\u20133017","DOI":"10.1109\/CVPRW50498.2020.00359"},{"key":"2536_CR6","unstructured":"Dao T, Gu A, Ratner A, et\u00a0al (2019) A kernel theory of modern data augmentation. In International conference on machine learning, PMLR, pp.\u00a01528\u20131537"},{"key":"2536_CR7","unstructured":"DeVries T, Taylor GW (2017) Improved regularization of convolutional neural networks with cutout. arXiv preprint arXiv:1708.04552"},{"key":"2536_CR8","unstructured":"Du X, Sun Y, Zhu J, & Li, Y. (2024) Dream the impossible: Outlier imagination with diffusion models. Advances in Neural Information Processing Systems 36"},{"key":"2536_CR9","unstructured":"Gastaldi X (2017) Shake-shake regularization. arXiv preprint arXiv:1705.07485"},{"key":"2536_CR10","doi-asserted-by":"crossref","unstructured":"Hataya R, Zdenek J, Yoshizoe K, & Nakayama, H. (2020) Faster autoaugment: Learning augmentation strategies using backpropagation. In: European Conference on Computer Vision (ECCV), Springer, pp.\u00a01\u201316","DOI":"10.1007\/978-3-030-58595-2_1"},{"key":"2536_CR11","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, et\u00a0al (2016) Deep residual learning for image recognition. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR) pp 770\u2013778","DOI":"10.1109\/CVPR.2016.90"},{"key":"2536_CR12","doi-asserted-by":"crossref","unstructured":"Hoffer E, Ben-Nun T, Hubara I, et\u00a0al (2020) Augment your batch: Improving generalization through instance repetition. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp.\u00a08129\u20138138","DOI":"10.1109\/CVPR42600.2020.00815"},{"key":"2536_CR13","unstructured":"Ho D, Liang E, Chen X, et\u00a0al (2019) Population based augmentation: Efficient learning of augmentation policy schedules. In International Conference on Machine Learning (ICML), PMLR, pp.\u00a02731\u20132741"},{"key":"2536_CR14","unstructured":"Hounie I, Chamon LF, Ribeiro A (2023) Automatic data augmentation via invariance-constrained learning. In International Conference on Machine Learning (ICML), PMLR, pp.\u00a013410\u201313433"},{"key":"2536_CR15","doi-asserted-by":"crossref","unstructured":"Hou C, Zhang J, Zhou T (2023) When to learn what: Model-adaptive data augmentation curriculum. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp 1717\u20131728","DOI":"10.1109\/ICCV51070.2023.00165"},{"key":"2536_CR16","unstructured":"Krizhevsky A, Hinton G, et\u00a0al (2009) Learning multiple layers of features from tiny images"},{"key":"2536_CR17","unstructured":"Kuriyama K (2023) Latentaugment: Dynamically optimized latent probabilities of data augmentation. arXiv preprint arXiv:2305.02668"},{"key":"2536_CR18","unstructured":"Kurtulu\u015f E, Li Z, Dauphin Y, et\u00a0al (2023) Tied-augment: Controlling representation similarity improves data augmentation. In: International Conference on Machine Learning (ICML), PMLR, pp 17994\u201318007"},{"key":"2536_CR19","doi-asserted-by":"crossref","unstructured":"Li Y, Hu G, Wang Y, et\u00a0al (2020) Dada: Differentiable automatic data augmentation pp 580\u2013595","DOI":"10.1007\/978-3-030-58542-6_35"},{"key":"2536_CR20","unstructured":"Lim S, Kim I, Kim T, et\u00a0al (2019) Fast autoaugment. In: Advances in Neural Information Processing Systems (NeurIPS)"},{"key":"2536_CR21","doi-asserted-by":"crossref","unstructured":"Lin TY, Goyal P, Girshick R, et\u00a0al (2017) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp 2980\u20132988","DOI":"10.1109\/ICCV.2017.324"},{"key":"2536_CR22","doi-asserted-by":"crossref","unstructured":"Lin TY, Maire M, Belongie S, et\u00a0al (2014) Microsoft coco: Common objects in context. In: Computer Vision\u2013ECCV 2014: 13th European Conference, Zurich, Switzerland, September 6-12, 2014, Proceedings, Part V 13, Springer, pp 740\u2013755","DOI":"10.1007\/978-3-319-10602-1_48"},{"key":"2536_CR23","unstructured":"LingChen TC, Khonsari A, Lashkari A, et\u00a0al (2020) Uniformaugment: A search-free probabilistic data augmentation approach. arXiv preprint arXiv:2003.14348"},{"key":"2536_CR24","doi-asserted-by":"crossref","unstructured":"Lin C, Guo M, Li C, et\u00a0al (2019) Online hyper-parameter learning for auto-augmentation strategy. Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV) pp 6578\u20136587","DOI":"10.1109\/ICCV.2019.00668"},{"key":"2536_CR25","doi-asserted-by":"crossref","unstructured":"Lin S, Zhang Z, Li X, et\u00a0al (2023) Selectaugment: hierarchical deterministic sample selection for data augmentation. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 1604\u20131612","DOI":"10.1609\/aaai.v37i2.25247"},{"key":"2536_CR26","doi-asserted-by":"crossref","unstructured":"Liu A, Huang Z, Huang Z, et\u00a0al (2021a) Direct differentiable augmentation search. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV), pp 12219\u201312228","DOI":"10.1109\/ICCV48922.2021.01200"},{"key":"2536_CR27","doi-asserted-by":"crossref","unstructured":"Liu Z, Lin Y, Cao Y, et\u00a0al (2021b) Swin transformer: Hierarchical vision transformer using shifted windows. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV), pp 10012\u201310022","DOI":"10.1109\/ICCV48922.2021.00986"},{"key":"2536_CR28","doi-asserted-by":"crossref","unstructured":"Liu Z, Miao Z, Zhan X, et\u00a0al (2019) Large-scale long-tailed recognition in an open world. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 2537\u20132546","DOI":"10.1109\/CVPR.2019.00264"},{"key":"2536_CR29","unstructured":"Liu Y, Tian Y, Zhao Y, et\u00a0al (2024) Vmamba: Visual state space model. Advances in Neural Information Processing Systems (NeurIPS)"},{"key":"2536_CR30","doi-asserted-by":"crossref","unstructured":"Lu S, Zhao M, Yuan S, et\u00a0al (2023) Bda: Bandit-based transferable autoaugment. In: Proceedings of the 2023 SIAM International Conference on Data Mining (SDM), SIAM, pp 550\u2013558","DOI":"10.1137\/1.9781611977653.ch62"},{"key":"2536_CR31","doi-asserted-by":"crossref","unstructured":"Marrie J, Arbel M, Larlus D, et\u00a0al (2023) Slack: Stable learning of augmentations with cold-start and kl regularization. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp 24306\u201324314","DOI":"10.1109\/CVPR52729.2023.02328"},{"key":"2536_CR32","unstructured":"Mehta S, Naderiparizi S, Faghri F, et\u00a0al (2022) Rangeaugment: Efficient online augmentation with range learning. arXiv preprint arXiv:2212.10553"},{"key":"2536_CR33","unstructured":"Miao N, Rainforth T, Mathieu E, et\u00a0al (2023) Learning instance\u2013specific augmentations by capturing local invariances. In Proceedings of the 40th International Conference on Machine Learning, pp.\u00a024720\u201324736"},{"key":"2536_CR34","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 (ICCV), pp.\u00a0774\u2013782","DOI":"10.1109\/ICCV48922.2021.00081"},{"key":"2536_CR35","doi-asserted-by":"crossref","unstructured":"Nguyen A, Yosinski J, Clune J (2015) Deep neural networks are easily fooled: High confidence predictions for unrecognizable images. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 427\u2013436","DOI":"10.1109\/CVPR.2015.7298640"},{"issue":"40","key":"2536_CR36","doi-asserted-by":"publisher","first-page":"24652","DOI":"10.1073\/pnas.2015509117","volume":"117","author":"V Papyan","year":"2020","unstructured":"Papyan, V., Han, X., & Donoho, D. L. (2020). Prevalence of neural collapse during the terminal phase of deep learning training. Proceedings of the National Academy of Sciences, 117(40), 24652\u201324663.","journal-title":"Proceedings of the National Academy of Sciences"},{"key":"2536_CR37","unstructured":"Ratner AJ, Ehrenberg H, Hussain Z, et\u00a0al (2017) Learning to compose domain-specific transformations for data augmentation. In: Advances in Neural Information Processing Systems (NeurIPS), vol\u00a030. Curran Associates, Inc."},{"key":"2536_CR38","doi-asserted-by":"publisher","first-page":"211","DOI":"10.1007\/s11263-015-0816-y","volume":"115","author":"O Russakovsky","year":"2015","unstructured":"Russakovsky, O., Deng, J., Su, H., et al. (2015). Imagenet large scale visual recognition challenge. International Journal of Computer Vision, 115, 211\u2013252.","journal-title":"International Journal of Computer Vision"},{"key":"2536_CR39","doi-asserted-by":"crossref","unstructured":"Shi J, Ghazzai H, Massoud Y (2023) Differentiable image data augmentation and its applications: A survey. IEEE Transactions on Pattern Analysis and Machine Intelligence","DOI":"10.36227\/techrxiv.21805797"},{"key":"2536_CR40","doi-asserted-by":"crossref","unstructured":"Suzuki T (2022) Teachaugment: Data augmentation optimization using teacher knowledge. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 10904\u201310914","DOI":"10.1109\/CVPR52688.2022.01063"},{"key":"2536_CR41","doi-asserted-by":"crossref","unstructured":"Szegedy C, Vanhoucke V, Ioffe S, et\u00a0al (2016) Rethinking the inception architecture for computer vision. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp 2818\u20132826","DOI":"10.1109\/CVPR.2016.308"},{"key":"2536_CR42","doi-asserted-by":"crossref","unstructured":"Tang Z, Peng X, Li T, et\u00a0al (2019) Adatransform: Adaptive data transformation. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp 2998\u20133006","DOI":"10.1109\/ICCV.2019.00309"},{"key":"2536_CR43","first-page":"19088","volume":"33","author":"K Tian","year":"2020","unstructured":"Tian, K., Lin, C., Sun, M., et al. (2020). Improving auto-augment via augmentation-wise weight sharing. Advances in Neural Information Processing Systems (NeurIPS), 33, 19088\u201319098.","journal-title":"Advances in Neural Information Processing Systems (NeurIPS)"},{"key":"2536_CR44","unstructured":"Touvron H, Cord M, Douze M, et\u00a0al (2021) Training data-efficient image transformers & distillation through attention. In: International Conference on Machine Learning (ICML), PMLR, pp 10347\u201310357"},{"key":"2536_CR45","unstructured":"Van\u00a0der Maaten L, Hinton G (2008) Visualizing data using t-sne. Journal of Machine Learning Research 9(11)"},{"key":"2536_CR46","unstructured":"Wang Z, Guo Y, Li Q, et\u00a0al (2023) Dualaug: Exploiting additional heavy augmentation with ood data rejection. arXiv preprint arXiv:2310.08139"},{"key":"2536_CR47","doi-asserted-by":"crossref","unstructured":"Wei L, Xiao A, Xie L, et\u00a0al (2020) Circumventing outliers of autoaugment with knowledge distillation. In: European Conference on Computer Vision (ECCV), Springer, pp 608\u2013625","DOI":"10.1007\/978-3-030-58580-8_36"},{"key":"2536_CR48","unstructured":"Wightman R, Touvron H, J\u00e9gou H (2021) Resnet strikes back: An improved training procedure in timm. In: NeurIPS 2021 Workshop on ImageNet: Past, Present, and Future"},{"key":"2536_CR49","doi-asserted-by":"crossref","unstructured":"Yang S, Li P, Xiong X, et\u00a0al (2024a) Adaaugment: A tuning-free and adaptive approach to enhance data augmentation. arXiv preprint arXiv:2405.11467","DOI":"10.1109\/TIP.2025.3592538"},{"key":"2536_CR50","doi-asserted-by":"crossref","unstructured":"Yang S, Shen F, Zhao J (2024b) Entaugment: Entropy-driven adaptive data augmentation framework for image classification. In: European Conference on Computer Vision, Springer, pp 197\u2013214","DOI":"10.1007\/978-3-031-72848-8_12"},{"key":"2536_CR51","doi-asserted-by":"crossref","unstructured":"Yun S, Han D, Oh SJ, et\u00a0al (2019) Cutmix: Regularization strategy to train strong classifiers with localizable features. Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV) pp 6022\u20136031","DOI":"10.1109\/ICCV.2019.00612"},{"key":"2536_CR52","doi-asserted-by":"crossref","unstructured":"Zagoruyko S, Komodakis N (2016) Wide residual networks. In: Procedings of the British Machine Vision Conference (BMVC), British Machine Vision Association","DOI":"10.5244\/C.30.87"},{"key":"2536_CR53","unstructured":"Zhang H, Cisse M, Dauphin YN, et\u00a0al (2018) Mixup: Beyond empirical risk minimization. In: International Conference on Learning Representations (ICLR)"},{"key":"2536_CR54","unstructured":"Zhang X, Wang Q, Zhang J, et\u00a0al (2019) Adversarial autoaugment. In: International Conference on Learning Representations (ICLR)"},{"key":"2536_CR55","doi-asserted-by":"crossref","unstructured":"Zhao M, Lu S, Wang Z, et\u00a0al (2022) La3: Efficient label-aware autoaugment. In: European Conference on Computer Vision (ECCV), pp 262\u2013277","DOI":"10.1007\/978-3-031-19803-8_16"},{"key":"2536_CR56","unstructured":"Zheng Y, Zhang Z, Yan S, et\u00a0al (2022) Deep autoaugmentation. In: International Conference on Learning Representations (ICLR)"},{"key":"2536_CR57","doi-asserted-by":"crossref","unstructured":"Zhong Z, Zheng L, Kang G, et\u00a0al (2020) Random erasing data augmentation. In: Proceedings of the AAAI conference on artificial intelligence (AAAI), pp 13001\u201313008","DOI":"10.1609\/aaai.v34i07.7000"},{"key":"2536_CR58","doi-asserted-by":"crossref","unstructured":"Zhou F, Li J, Xie C, et\u00a0al (2021) Metaaugment: Sample-aware data augmentation policy learning. In: Proceedings of the AAAI conference on artificial intelligence (AAAI), pp 11097\u201311105","DOI":"10.1609\/aaai.v35i12.17324"}],"container-title":["International Journal of Computer Vision"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11263-025-02536-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11263-025-02536-x\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11263-025-02536-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,12]],"date-time":"2025-11-12T06:27:04Z","timestamp":1762928824000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11263-025-02536-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,8,8]]},"references-count":58,"journal-issue":{"issue":"11","published-print":{"date-parts":[[2025,11]]}},"alternative-id":["2536"],"URL":"https:\/\/doi.org\/10.1007\/s11263-025-02536-x","relation":{},"ISSN":["0920-5691","1573-1405"],"issn-type":[{"value":"0920-5691","type":"print"},{"value":"1573-1405","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,8,8]]},"assertion":[{"value":"15 January 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 July 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 August 2025","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 declare no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}]}}