{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T04:57:41Z","timestamp":1760245061411,"version":"3.40.3"},"publisher-location":"Cham","reference-count":49,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031200731"},{"type":"electronic","value":"9783031200748"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-3-031-20074-8_23","type":"book-chapter","created":{"date-parts":[[2022,11,11]],"date-time":"2022-11-11T20:23:11Z","timestamp":1668198191000},"page":"397-413","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["When Deep Classifiers Agree: Analyzing Correlations Between Learning Order and\u00a0Image Statistics"],"prefix":"10.1007","author":[{"given":"Iuliia","family":"Pliushch","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Martin","family":"Mundt","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nicolas","family":"Lupp","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Visvanathan","family":"Ramesh","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,11,12]]},"reference":[{"key":"23_CR1","doi-asserted-by":"crossref","unstructured":"Ahmed, N., Natarajan, T., Rao, K.R.: Discrete cosine transform. IEEE Trans. Comput. C-24(1), 90\u201393 (1974)","DOI":"10.1109\/T-C.1974.223784"},{"issue":"11","key":"23_CR2","doi-asserted-by":"publisher","first-page":"2189","DOI":"10.1109\/TPAMI.2012.28","volume":"34","author":"B Alexe","year":"2012","unstructured":"Alexe, B., Deselaers, T., Ferrari, V.: Measuring the objectness of image windows. IEEE Trans. Pattern Anal. Mach. Intell. (TPAMI) 34(11), 2189\u20132202 (2012)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell. (TPAMI)"},{"key":"23_CR3","unstructured":"Arpit, D., et al.: A closer look at memorization in deep networks. In: International Conference on Machine Learning (ICML) (2017)"},{"key":"23_CR4","doi-asserted-by":"crossref","unstructured":"Bengio, Y., Louradour, J., Collobert, R., Weston, J.: Curriculum learning. In: International Conference on Machine Learning (ICML) (2009)","DOI":"10.1145\/1553374.1553380"},{"key":"23_CR5","doi-asserted-by":"crossref","unstructured":"Berg, A.C., et al.: Understanding and predicting importance in images. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2012)","DOI":"10.1109\/CVPR.2012.6248100"},{"issue":"5","key":"23_CR6","doi-asserted-by":"publisher","first-page":"423","DOI":"10.1016\/0895-4356(93)90018-V","volume":"46","author":"T Byrt","year":"1993","unstructured":"Byrt, T., Bishop, J., Carlin, J.B.: Bias, prevalence and kappa. J. Clin. Epidemiol. 46(5), 423\u2013429 (1993)","journal-title":"J. Clin. Epidemiol."},{"issue":"3\u20134","key":"23_CR7","doi-asserted-by":"publisher","first-page":"429","DOI":"10.1007\/s10472-017-9564-8","volume":"81","author":"O Caelen","year":"2017","unstructured":"Caelen, O.: A Bayesian interpretation of the confusion matrix. Ann. Math. Artif. Intell. 81(3\u20134), 429\u2013450 (2017)","journal-title":"Ann. Math. Artif. Intell."},{"key":"23_CR8","doi-asserted-by":"crossref","unstructured":"Caputo, B., Hayman, E., Mallikarjuna, P.: Class-specific material categorisation. In: International Conference on Computer Vision (ICCV) (2005)","DOI":"10.1109\/ICCV.2005.54"},{"key":"23_CR9","doi-asserted-by":"crossref","unstructured":"Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: ImageNet: a large-scale hierarchical image database. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2009)","DOI":"10.1109\/CVPR.2009.5206848"},{"issue":"1","key":"23_CR10","doi-asserted-by":"publisher","first-page":"98","DOI":"10.1007\/s11263-014-0733-5","volume":"111","author":"M Everingham","year":"2015","unstructured":"Everingham, M., Eslami, S.M., Van Gool, L., Williams, C.K., Winn, J., Zisserman, A.: The pascal visual object classes challenge: a retrospective. Int. J. Comput. Vision 111(1), 98\u2013136 (2015)","journal-title":"Int. J. Comput. Vision"},{"issue":"2","key":"23_CR11","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, C.K.I., Winn, J., Zisserman, A.: The pascal visual object classes (VOC) challenge. Int. J. Comput. Vision 88(2), 303\u2013338 (2010)","journal-title":"Int. J. Comput. Vision"},{"key":"23_CR12","doi-asserted-by":"publisher","first-page":"167","DOI":"10.1023\/B:VISI.0000022288.19776.77","volume":"59","author":"PF Felzenszwalb","year":"2004","unstructured":"Felzenszwalb, P.F., Huttenlocher, D.P.: Efficient Graph-Based Image Segmentation. Int. J. Comput. Vision 59, 167\u2013181 (2004)","journal-title":"Int. J. Comput. Vision"},{"issue":"4","key":"23_CR13","doi-asserted-by":"publisher","first-page":"511","DOI":"10.1364\/JOSA.62.000511","volume":"62","author":"BR Frieden","year":"1972","unstructured":"Frieden, B.R.: Restoring with maximum likelihood and maximum entropy. J. Optical Soc. America (JOSA) 62(4), 511\u2013518 (1972)","journal-title":"J. Optical Soc. America (JOSA)"},{"key":"23_CR14","unstructured":"Geirhos, R., Michaelis, C., Wichmann, F.A., Rubisch, P., Bethge, M., Brendel, W.: Imagenet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness. International Conference on Learning Representations (ICLR) (2019)"},{"key":"23_CR15","unstructured":"Hacohen, G., Choshen, L., Weinshall, D.: Let\u2019s agree to agree: neural networks share classification order on real datasets. In: International Conference on Learning Representations (ICLR) (2020)"},{"key":"23_CR16","unstructured":"Hacohen, G., Weinshall, D.: On the power of curriculum learning in training deep networks. In: International Conference on Machine Learning (ICML) (2019)"},{"key":"23_CR17","first-page":"1","volume":"23","author":"G Hacohen","year":"2022","unstructured":"Hacohen, G., Weinshall, D.: Principal components bias in over-parameterizyed linear models, and its manifestation in deep neural networks. J. Mach. Learn. Res. 23, 1\u201346 (2022)","journal-title":"J. Mach. Learn. Res."},{"issue":"1","key":"23_CR18","doi-asserted-by":"publisher","first-page":"23","DOI":"10.20982\/tqmp.08.1.p023","volume":"8","author":"KA Hallgren","year":"2012","unstructured":"Hallgren, K.A.: Computing Inter-Rater Reliability for Observational Data: An Overview and Tutorial. Tutor Quant Methods Psychol. 8(1), 23\u201334 (2012)","journal-title":"Tutor Quant Methods Psychol."},{"key":"23_CR19","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"23_CR20","unstructured":"Hermann, K.L., Chen, T., Kornblith, S.: The origins and prevalence of texture bias in convolutional neural networks. In: Neural Information Processing Systems (NeurIPS) 34 (2020)"},{"key":"23_CR21","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"340","DOI":"10.1007\/978-3-642-33712-3_25","volume-title":"Computer Vision \u2013 ECCV 2012","author":"D Hoiem","year":"2012","unstructured":"Hoiem, D., Chodpathumwan, Y., Dai, Q.: Diagnosing error in object detectors. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012. LNCS, vol. 7574, pp. 340\u2013353. Springer, Heidelberg (2012). https:\/\/doi.org\/10.1007\/978-3-642-33712-3_25"},{"key":"23_CR22","doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., Van Der Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2261\u20132269 (2017)","DOI":"10.1109\/CVPR.2017.243"},{"key":"23_CR23","unstructured":"Ilyas, A., Santurkar, S., Tsipras, D., Engstrom, L., Tran, B., Madry, A.: Adversarial examples are not bugs, they are features. In: Neural Information Processing Systems (NeurIPS) (2019)"},{"key":"23_CR24","doi-asserted-by":"crossref","unstructured":"Ionescu, R.T., Alexe, B., Leordeanu, M., Popescu, M., Papadopoulos, D.P., Ferrari, V.: How hard can it be? estimating the difficulty of visual search in an image. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2157\u20132166 (2016)","DOI":"10.1109\/CVPR.2016.237"},{"issue":"7","key":"23_CR25","doi-asserted-by":"publisher","first-page":"1469","DOI":"10.1109\/TPAMI.2013.200","volume":"36","author":"P Isola","year":"2014","unstructured":"Isola, P., Xiao, J., Parikh, D., Torralba, A., Oliva, A.: What makes a photograph memorable? IEEE Trans. Pattern Anal. Mach. Intell. (TPAMI) 36(7), 1469\u20131482 (2014)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell. (TPAMI)"},{"key":"23_CR26","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"799","DOI":"10.1007\/978-3-319-10578-9_52","volume-title":"Computer Vision \u2013 ECCV 2014","author":"P Isola","year":"2014","unstructured":"Isola, P., Zoran, D., Krishnan, D., Adelson, E.H.: Crisp boundary detection using pointwise mutual information. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8691, pp. 799\u2013814. Springer, Cham (2014). https:\/\/doi.org\/10.1007\/978-3-319-10578-9_52"},{"key":"23_CR27","doi-asserted-by":"crossref","unstructured":"Jiang, L., Meng, D., Zhao, Q., Shan, S., Hauptmann, A.G.: Self-paced curriculum learning. In: Proceedings of the National Conference on Artificial Intelligence, vol. 4, pp. 2694\u20132700 (2015)","DOI":"10.1609\/aaai.v29i1.9608"},{"key":"23_CR28","unstructured":"Krizhevsky, A.: Learning Multiple Layers of Features from Tiny Images. Technical report Toronto (2009)"},{"key":"23_CR29","unstructured":"Kumar, M., Packer, B., Koller, D., Kumar, P., Packer, B., Koller, D.: Self-paced learning for latent variable models. In: Neural Information Processing Systems (NeurIPS) (2010)"},{"issue":"11","key":"23_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.: Gradient-based learning applied to document recognition. Proc. IEEE 86(11), 2278\u20132323 (1998)","journal-title":"Proc. IEEE"},{"key":"23_CR31","doi-asserted-by":"crossref","unstructured":"Lee, Y.J., Grauman, K.: Learning the easy things first: Self-paced visual category discovery. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1721\u20131728 (2011)","DOI":"10.1109\/CVPR.2011.5995523"},{"key":"23_CR32","unstructured":"Li, Y., Yosinski, J., Clune, J., Lipson, H., Hopcroft, J.: Convergent Learning: do different neural networks learn the same representations? In: International Conference on Learning Representations (ICLR) (2016)"},{"key":"23_CR33","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"484","DOI":"10.1007\/978-3-642-23199-5_36","volume-title":"Machine Learning and Data Mining in Pattern Recognition","author":"D Liu","year":"2011","unstructured":"Liu, D., Xiong, Y., Pulli, K., Shapiro, L.: Estimating image segmentation difficulty. In: Perner, P. (ed.) MLDM 2011. LNCS (LNAI), vol. 6871, pp. 484\u2013495. Springer, Heidelberg (2011). https:\/\/doi.org\/10.1007\/978-3-642-23199-5_36"},{"key":"23_CR34","unstructured":"Maennel, H., et al.: What do neural networks learn when trained with random labels? In: Neural Information Processing Systems (NeurIPS) (2020)"},{"key":"23_CR35","unstructured":"Mangalam, K., Prabhu, V.: Do deep neural networks learn shallow learnable examples first? In: International Conference on Machine Learning (ICML), Deep Phenomena Workshop (2019)"},{"key":"23_CR36","unstructured":"Ortiz-Jim\u00e9nez, G., Modas, A., Moosavi-Dezfooli, S.M., Frossard, P.: Hold me tight! Influence of discriminative features on deep network boundaries, Neural Information Processing Systems (NeurIPS) (2020)"},{"key":"23_CR37","doi-asserted-by":"crossref","unstructured":"Peterson, J.C., Battleday, R.M., Griffiths, T.L., Russakovsky, O.: Human uncertainty makes classification more robust. In: International Conference on Computer Vision (ICCV) (2019)","DOI":"10.1109\/ICCV.2019.00971"},{"issue":"1","key":"23_CR38","doi-asserted-by":"publisher","first-page":"0151","DOI":"10.1371\/journal.pcbi.0040027","volume":"4","author":"N Pinto","year":"2008","unstructured":"Pinto, N., Cox, D.D., DiCarlo, J.J.: Why is real-world visual object recognition hard? PLoS Comput. Biol. 4(1), 0151\u20130156 (2008)","journal-title":"PLoS Comput. Biol."},{"key":"23_CR39","doi-asserted-by":"crossref","unstructured":"Russakovsky, O., Deng, J., Huang, Z., Berg, A.C., Fei-Fei, L.: Detecting avocados to Zucchinis: what have we done, and where are we going? In: International Conference on Computer Vision (ICCV) (2013)","DOI":"10.1109\/ICCV.2013.258"},{"issue":"3","key":"23_CR40","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., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., Berg, A.C., Fei-Fei, L.: ImageNet large scale visual recognition challenge. Int. J. Comput. Vision 115(3), 211\u2013252 (2015)","journal-title":"Int. J. Comput. Vision"},{"key":"23_CR41","unstructured":"Shah, H., Tamuly, K., Raghunathan, A., Jain, P., Netrapalli, P.: The pitfalls of simplicity bias in neural networks. In: Neural Information Processing Systems (NeurIPS) (2020)"},{"key":"23_CR42","unstructured":"Shwartz-Ziv, R., Tishby, N.: Opening the Black Box of Deep Neural Networks via Information. Why & when Deep Learning works: looking inside Deep Learning ICRI-CI (2017)"},{"key":"23_CR43","unstructured":"Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. In: International Conference on Learning Representations (ICLR) (2015)"},{"key":"23_CR44","doi-asserted-by":"publisher","first-page":"111","DOI":"10.1093\/mnras\/211.1.111","volume":"211","author":"J Skilling","year":"1984","unstructured":"Skilling, J., Bryan, R.: Maximum entropy image reconstruction: general algorithm. Mon. Not. R. Astron. Soc. 211, 111\u2013124 (1984)","journal-title":"Mon. Not. R. Astron. Soc."},{"key":"23_CR45","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"523","DOI":"10.1007\/978-3-540-88682-2_40","volume-title":"Computer Vision \u2013 ECCV 2008","author":"M Spain","year":"2008","unstructured":"Spain, M., Perona, P.: Some objects are more equal than others: measuring and predicting importance. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008. LNCS, vol. 5302, pp. 523\u2013536. Springer, Heidelberg (2008). https:\/\/doi.org\/10.1007\/978-3-540-88682-2_40"},{"key":"23_CR46","doi-asserted-by":"crossref","unstructured":"Vijayanarasimhan, S., Grauman, K.: What\u2019s it going to cost you? Predicting effort vs. informativeness for multi-label image annotations. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2009)","DOI":"10.1109\/CVPR.2009.5206705"},{"key":"23_CR47","unstructured":"Wang, L., Hu, L., Gu, J., Wu, Y., Hu, Z., He, K., Hopcroft, J.: Towards understanding learning representations: to what extent do different neural networks learn the same representation. In: Neural Information Processing Systems (NeurIPS) (2018)"},{"key":"23_CR48","doi-asserted-by":"crossref","unstructured":"Yang, K., Qinami, K., Fei-Fei, L., Deng, J., Russakovsky, O.: Towards fairer datasets: Filtering and balancing the distribution of the people subtree in the ImageNet hierarchy. In: Conference on Fairness, Accountability, and Transparency (FAT), pp. 547\u2013558 (2020)","DOI":"10.1145\/3351095.3375709"},{"key":"23_CR49","unstructured":"Zhang, C., Bengio, S., Hardt, M., Recht, B., Vinyals, O.: Understanding deep learning requires rethinking generalization. In: International Conference on Learning Representations (ICLR) (2017)"}],"container-title":["Lecture Notes in Computer Science","Computer Vision \u2013 ECCV 2022"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-20074-8_23","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,11,11]],"date-time":"2022-11-11T20:29:22Z","timestamp":1668198562000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-20074-8_23"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031200731","9783031200748"],"references-count":49,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-20074-8_23","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"12 November 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ECCV","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"European Conference on Computer Vision","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Tel Aviv","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Israel","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 October 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 October 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"eccv2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/eccv2022.ecva.net\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"5804","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"1645","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"28% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3.21","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3.91","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}