{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,23]],"date-time":"2025-09-23T00:00:33Z","timestamp":1758585633380,"version":"3.44.0"},"reference-count":40,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2024,2,1]],"date-time":"2024-02-01T00:00:00Z","timestamp":1706745600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2024,2,1]],"date-time":"2024-02-01T00:00:00Z","timestamp":1706745600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2024,2,1]],"date-time":"2024-02-01T00:00:00Z","timestamp":1706745600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2024,2,1]],"date-time":"2024-02-01T00:00:00Z","timestamp":1706745600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2024,2,1]],"date-time":"2024-02-01T00:00:00Z","timestamp":1706745600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2024,2,1]],"date-time":"2024-02-01T00:00:00Z","timestamp":1706745600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2024,2,1]],"date-time":"2024-02-01T00:00:00Z","timestamp":1706745600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Pattern Recognition"],"published-print":{"date-parts":[[2024,2]]},"DOI":"10.1016\/j.patcog.2023.110026","type":"journal-article","created":{"date-parts":[[2023,10,5]],"date-time":"2023-10-05T11:55:18Z","timestamp":1696506918000},"page":"110026","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":2,"special_numbering":"C","title":["Customized meta-dataset for automatic classifier accuracy evaluation"],"prefix":"10.1016","volume":"146","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1363-5318","authenticated-orcid":false,"given":"Yan","family":"Huang","sequence":"first","affiliation":[]},{"given":"Zhang","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Yan","family":"Huang","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5641-2483","authenticated-orcid":false,"given":"Qiang","family":"Wu","sequence":"additional","affiliation":[]},{"given":"Han","family":"Huang","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9309-3407","authenticated-orcid":false,"given":"Yi","family":"Zhong","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5224-8647","authenticated-orcid":false,"given":"Liang","family":"Wang","sequence":"additional","affiliation":[]}],"member":"78","reference":[{"key":"10.1016\/j.patcog.2023.110026_b1","series-title":"Computer Vision and Pattern Recognition","first-page":"15069","article-title":"Are labels always necessary for classifier accuracy evaluation?","author":"Deng","year":"2021"},{"key":"10.1016\/j.patcog.2023.110026_b2","article-title":"AutoEval: Are labels always necessary for classifier accuracy evaluation","author":"Deng","year":"2021","journal-title":"Trans. Pattern Recognit. Mach. Intell."},{"key":"10.1016\/j.patcog.2023.110026_b3","unstructured":"W. Deng, S. Gould, L. Zheng, What Does Rotation Prediction Tell Us about Classifier Accuracy under Varying Testing Environments?, in: International Conference on Machine Learning, 2021, pp. 2579\u20132589."},{"key":"10.1016\/j.patcog.2023.110026_b4","series-title":"Computer Vision and Pattern Recognition","first-page":"2414","article-title":"Image style transfer using convolutional neural networks","author":"Gatys","year":"2016"},{"key":"10.1016\/j.patcog.2023.110026_b5","doi-asserted-by":"crossref","unstructured":"K. Zhou, Y. Yang, T. Hospedales, T. Xiang, Learning to generate novel domains for domain generalization, in: European Conference on Computer Vision, 2020, pp. 561\u2013578.","DOI":"10.1007\/978-3-030-58517-4_33"},{"key":"10.1016\/j.patcog.2023.110026_b6","doi-asserted-by":"crossref","unstructured":"Y. Yao, L. Zheng, X. Yang, M. Naphade, T. Gedeon, Simulating content consistent vehicle datasets with attribute descent, in: European Conference on Computer Vision, 2020, pp. 775\u2013791.","DOI":"10.1007\/978-3-030-58539-6_46"},{"key":"10.1016\/j.patcog.2023.110026_b7","series-title":"Computer Vision and Pattern Recognition","first-page":"608","article-title":"Dissecting person re-identification from the viewpoint of viewpoint","author":"Sun","year":"2019"},{"key":"10.1016\/j.patcog.2023.110026_b8","series-title":"Computer Vision and Pattern Recognition","first-page":"113","article-title":"Autoaugment: Learning augmentation strategies from data","author":"Cubuk","year":"2019"},{"year":"2015","series-title":"John Riccitiello sets out to identify the engine of growth for Unity Technologies (interview)","author":"Riccitiello","key":"10.1016\/j.patcog.2023.110026_b9"},{"key":"10.1016\/j.patcog.2023.110026_b10","article-title":"Mutual variational inference: An indirect variational inference approach for unsupervised domain adaptation","author":"Chen","year":"2021","journal-title":"IEEE Trans. Cybern."},{"key":"10.1016\/j.patcog.2023.110026_b11","unstructured":"J. Hoffman, E. Tzeng, T. Park, J.-Y. Zhu, P. Isola, K. Saenko, A. Efros, T. Darrell, Cycada: Cycle-consistent adversarial domain adaptation, in: International Conference on Machine Learning, 2018, pp. 1989\u20131998."},{"key":"10.1016\/j.patcog.2023.110026_b12","doi-asserted-by":"crossref","unstructured":"J.-Y. Zhu, T. Park, P. Isola, A.A. Efros, Unpaired image-to-image translation using cycle-consistent adversarial networks, in: Proceedings of the IEEE International Conference on Computer Vision, 2017, pp. 2223\u20132232.","DOI":"10.1109\/ICCV.2017.244"},{"key":"10.1016\/j.patcog.2023.110026_b13","doi-asserted-by":"crossref","DOI":"10.1016\/j.patcog.2023.109416","article-title":"Cycle-object consistency for image-to-image domain adaptation","author":"Lin","year":"2023","journal-title":"Pattern Recognit."},{"key":"10.1016\/j.patcog.2023.110026_b14","series-title":"Computer Vision and Pattern Recognition","first-page":"994","article-title":"Image-image domain adaptation with preserved self-similarity and domain-dissimilarity for person re-identification","author":"Deng","year":"2018"},{"key":"10.1016\/j.patcog.2023.110026_b15","doi-asserted-by":"crossref","first-page":"2244","DOI":"10.1007\/s11263-021-01474-8","article-title":"Unsupervised domain adaptation with background shift mitigating for person re-identification","author":"Huang","year":"2021","journal-title":"Int. J. Comput. Vis."},{"year":"2016","series-title":"Fcns in the wild: Pixel-level adversarial and constraint-based adaptation","author":"Hoffman","key":"10.1016\/j.patcog.2023.110026_b16"},{"key":"10.1016\/j.patcog.2023.110026_b17","doi-asserted-by":"crossref","DOI":"10.1016\/j.patcog.2022.108911","article-title":"Learning intra-domain style-invariant representation for unsupervised domain adaptation of semantic segmentation","volume":"132","author":"Li","year":"2022","journal-title":"Pattern Recognit."},{"issue":"3","key":"10.1016\/j.patcog.2023.110026_b18","doi-asserted-by":"crossref","first-page":"450","DOI":"10.1016\/0047-259X(82)90077-X","article-title":"The Fr\u00e9chet distance between multivariate normal distributions","volume":"12","author":"Dowson","year":"1982","journal-title":"J. Multivar. Anal."},{"key":"10.1016\/j.patcog.2023.110026_b19","series-title":"ICCV","first-page":"1134","article-title":"Predicting with confidence on unseen distributions","author":"Guillory","year":"2021"},{"year":"2021","series-title":"Label-free model evaluation with semi-structured dataset representations","author":"Sun","key":"10.1016\/j.patcog.2023.110026_b20"},{"key":"10.1016\/j.patcog.2023.110026_b21","series-title":"Advances in Neural Information Processing Systems","first-page":"14980","article-title":"Detecting errors and estimating accuracy on unlabeled data with self-training ensembles","author":"Chen","year":"2021"},{"key":"10.1016\/j.patcog.2023.110026_b22","unstructured":"Y. Jiang, V. Nagarajan, C. Baek, J.Z. Kolter, Assessing Generalization of SGD via Disagreement, in: International Conference on Learning Representations, 2022."},{"key":"10.1016\/j.patcog.2023.110026_b23","unstructured":"M. Chen, K. Goel, N.S. Sohoni, F. Poms, K. Fatahalian, C. R\u00e9, Mandoline: Model evaluation under distribution shift, in: International Conference on Machine Learning, 2021, pp. 1617\u20131629."},{"key":"10.1016\/j.patcog.2023.110026_b24","unstructured":"S. Garg, S. Balakrishnan, Z.C. Lipton, B. Neyshabur, H. Sedghi, Leveraging unlabeled data to predict out-of-distribution performance, in: International Conference on Learning Representations, 2022."},{"key":"10.1016\/j.patcog.2023.110026_b25","unstructured":"C.-Y. Chuang, A. Torralba, S. Jegelka, Estimating generalization under distribution shifts via domain-invariant representations, in: International Conference on Machine Learning, 2020."},{"key":"10.1016\/j.patcog.2023.110026_b26","series-title":"NeurIPS","article-title":"Generative adversarial nets","author":"Goodfellow","year":"2014"},{"issue":"11","key":"10.1016\/j.patcog.2023.110026_b27","doi-asserted-by":"crossref","first-page":"2278","DOI":"10.1109\/5.726791","article-title":"Gradient-based learning applied to document recognition","volume":"86","author":"LeCun","year":"1998","journal-title":"Proc. IEEE"},{"key":"10.1016\/j.patcog.2023.110026_b28","unstructured":"Y. Netzer, T. Wang, A. Coates, A. Bissacco, B. Wu, A.Y. Ng, Reading digits in natural images with unsupervised feature learning, in: NeurIPS Workshop, 2011."},{"year":"2009","series-title":"Learning Multiple Layers of Features from Tiny Images","author":"Krizhevsky","key":"10.1016\/j.patcog.2023.110026_b29"},{"year":"2018","series-title":"Do CIFAR-10 classifiers generalize to CIFAR-10?","author":"Recht","key":"10.1016\/j.patcog.2023.110026_b30"},{"year":"2017","series-title":"In defense of the triplet loss for person re-identification","author":"Hermans","key":"10.1016\/j.patcog.2023.110026_b31"},{"issue":"5","key":"10.1016\/j.patcog.2023.110026_b32","doi-asserted-by":"crossref","first-page":"550","DOI":"10.1109\/34.291440","article-title":"A database for handwritten text recognition research","volume":"16","author":"Hull","year":"1994","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"10.1016\/j.patcog.2023.110026_b33","doi-asserted-by":"crossref","unstructured":"T.-Y. Lin, M. Maire, S. Belongie, J. Hays, P. Perona, D. Ramanan, P. Doll\u00e1r, C.L. Zitnick, Microsoft coco: Common objects in context, in: European Conference on Computer Vision, 2014, pp. 740\u2013755.","DOI":"10.1007\/978-3-319-10602-1_48"},{"key":"10.1016\/j.patcog.2023.110026_b34","article-title":"The PASCAL visual object classes challenge 2007 (VOC2007) results","author":"Everingham","year":"2008","journal-title":"Int. J. Comput. Vis."},{"year":"2007","series-title":"Caltech-256 Object Category Dataset","author":"Griffin","key":"10.1016\/j.patcog.2023.110026_b35"},{"key":"10.1016\/j.patcog.2023.110026_b36","series-title":"Computer Vision and Pattern Recognition","first-page":"248","article-title":"Imagenet: A large-scale hierarchical image database","author":"Deng","year":"2009"},{"key":"10.1016\/j.patcog.2023.110026_b37","series-title":"Computer Vision and Pattern Recognition","first-page":"770","article-title":"Deep residual learning for image recognition","author":"He","year":"2016"},{"key":"10.1016\/j.patcog.2023.110026_b38","first-page":"21464","article-title":"Energy-based out-of-distribution detection","author":"Liu","year":"2020","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.patcog.2023.110026_b39","series-title":"Computer Vision and Pattern Recognition","first-page":"8789","article-title":"Stargan: Unified generative adversarial networks for multi-domain image-to-image translation","author":"Choi","year":"2018"},{"key":"10.1016\/j.patcog.2023.110026_b40","unstructured":"P.W. Koh, S. Sagawa, H. Marklund, S.M. Xie, M. Zhang, A. Balsubramani, W. Hu, M. Yasunaga, R.L. Phillips, I. Gao, et al., Wilds: A benchmark of in-the-wild distribution shifts, in: International Conference on Machine Learning, 2021, pp. 5637\u20135664."}],"container-title":["Pattern Recognition"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0031320323007239?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0031320323007239?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2025,9,21]],"date-time":"2025-09-21T17:22:03Z","timestamp":1758475323000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0031320323007239"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,2]]},"references-count":40,"alternative-id":["S0031320323007239"],"URL":"https:\/\/doi.org\/10.1016\/j.patcog.2023.110026","relation":{},"ISSN":["0031-3203"],"issn-type":[{"type":"print","value":"0031-3203"}],"subject":[],"published":{"date-parts":[[2024,2]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Customized meta-dataset for automatic classifier accuracy evaluation","name":"articletitle","label":"Article Title"},{"value":"Pattern Recognition","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.patcog.2023.110026","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2023 Published by Elsevier Ltd.","name":"copyright","label":"Copyright"}],"article-number":"110026"}}