{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,12]],"date-time":"2026-06-12T17:07:27Z","timestamp":1781284047922,"version":"3.54.1"},"publisher-location":"Cham","reference-count":67,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031198267","type":"print"},{"value":"9783031198274","type":"electronic"}],"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\/978-3-031-19827-4_26","type":"book-chapter","created":{"date-parts":[[2022,11,1]],"date-time":"2022-11-01T14:42:19Z","timestamp":1667313739000},"page":"440-458","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":43,"title":["Improving Test-Time Adaptation Via\u00a0Shift-Agnostic Weight Regularization and\u00a0Nearest Source Prototypes"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2313-9243","authenticated-orcid":false,"given":"Sungha","family":"Choi","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0411-8407","authenticated-orcid":false,"given":"Seunghan","family":"Yang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1695-5894","authenticated-orcid":false,"given":"Seokeon","family":"Choi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2462-3854","authenticated-orcid":false,"given":"Sungrack","family":"Yun","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2022,11,2]]},"reference":[{"key":"26_CR1","doi-asserted-by":"crossref","unstructured":"Agarwal, P., Paudel, D.P., Zaech, J.N., Van Gool, L.: Unsupervised robust domain adaptation without source data. In: IEEE\/CVF Winter Conference on Applications of Computer Vision (WACV) (2022)","DOI":"10.1109\/WACV51458.2022.00286"},{"key":"26_CR2","doi-asserted-by":"crossref","unstructured":"Assran, M., Caron, M., Misra, I., Bojanowski, P., Joulin, A., Ballas, N., Rabbat, M.: Semi-supervised learning of visual features by non-parametrically predicting view assignments with support samples. In: International Conference on Computer Vision (ICCV) (2021)","DOI":"10.1109\/ICCV48922.2021.00833"},{"key":"26_CR3","unstructured":"Balaji, Y., Sankaranarayanan, S., Chellappa, R.: Metareg: towards domain generalization using meta-regularization. In: Advances in Neural Information Processing Systems (NeurIPS) (2018)"},{"key":"26_CR4","unstructured":"Bardes, A., Ponce, J., LeCun, Y.: VICreg: variance-invariance-covariance regularization for self-supervised learning. arXiv preprint arXiv:2105.04906 (2021)"},{"key":"26_CR5","doi-asserted-by":"crossref","unstructured":"Beery, S., Van Horn, G., Perona, P.: Recognition in terra incognita. In: European Conference on Computer Vision (ECCV) (2018)","DOI":"10.1007\/978-3-030-01270-0_28"},{"key":"26_CR6","unstructured":"Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning (ICML) (2020)"},{"key":"26_CR7","doi-asserted-by":"crossref","unstructured":"Choi, S., Kim, T., Jeong, M., Park, H., Kim, C.: Meta batch-instance normalization for generalizable person re-identification. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2021)","DOI":"10.1109\/CVPR46437.2021.00343"},{"key":"26_CR8","doi-asserted-by":"crossref","unstructured":"Choi, S., Jung, S., Yun, H., Kim, J.T., Kim, S., Choo, J.: RobustNet: improving domain generalization in urban-scene segmentation via instance selective whitening. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2021)","DOI":"10.1109\/CVPR46437.2021.01141"},{"key":"26_CR9","unstructured":"Croce, F., et al.: RobustBench: a standardized adversarial robustness benchmark. arXiv preprint arXiv:2010.09670 (2020)"},{"key":"26_CR10","doi-asserted-by":"crossref","unstructured":"Fang, C., Xu, Y., Rockmore, D.N.: Unbiased metric learning: on the utilization of multiple datasets and web images for softening bias. In: International Conference on Computer Vision (ICCV) (2013)","DOI":"10.1109\/ICCV.2013.208"},{"key":"26_CR11","unstructured":"Ganin, Y., Lempitsky, V.: Unsupervised domain adaptation by backpropagation. In: International Conference on Machine Learning (ICML) (2015)"},{"key":"26_CR12","doi-asserted-by":"crossref","unstructured":"Ganin, Y., et al.: Domain-adversarial training of neural networks. J. Mach. Learn. Res. (2016)","DOI":"10.1007\/978-3-319-58347-1_10"},{"key":"26_CR13","unstructured":"Gidaris, S., Singh, P., Komodakis, N.: Unsupervised representation learning by predicting image rotations. In: International Conference on Learning Representations (ICLR) (2018)"},{"key":"26_CR14","doi-asserted-by":"crossref","unstructured":"Gong, R., Li, W., Chen, Y., Gool, L.V.: DLOW: domain flow for adaptation and generalization. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2019)","DOI":"10.1109\/CVPR.2019.00258"},{"key":"26_CR15","unstructured":"Grandvalet, Y., Bengio, Y.: Semi-supervised learning by entropy minimization. In: Advances in Neural Information Processing Systems (NeurIPS) (2004)"},{"key":"26_CR16","unstructured":"Gulrajani, I., Lopez-Paz, D.: In search of lost domain generalization. In: International Conference on Learning Representations (ICLR) (2020)"},{"key":"26_CR17","doi-asserted-by":"crossref","unstructured":"Guo, Y., Shi, H., Kumar, A., Grauman, K., Rosing, T., Feris, R.: SpotTune: transfer learning through adaptive fine-tuning. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2019)","DOI":"10.1109\/CVPR.2019.00494"},{"key":"26_CR18","doi-asserted-by":"crossref","unstructured":"He, K., Fan, H., Wu, Y., Xie, S., Girshick, R.: Momentum contrast for unsupervised visual representation learning. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2020)","DOI":"10.1109\/CVPR42600.2020.00975"},{"key":"26_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) (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"26_CR20","unstructured":"Hendrycks, D., Dietterich, T.: Benchmarking neural network robustness to common corruptions and perturbations. In: International Conference on Learning Representations (ICLR) (2018)"},{"key":"26_CR21","unstructured":"Hendrycks, D., Mu, N., Cubuk, E.D., Zoph, B., Gilmer, J., Lakshminarayanan, B.: Augmix: A simple data processing method to improve robustness and uncertainty. In: International Conference on Learning Representations (ICLR) (2019)"},{"key":"26_CR22","unstructured":"Hoffman, J., et al.: CYCADA: cycle-consistent adversarial domain adaptation. In: International Conference on Machine Learning (ICML) (2018)"},{"key":"26_CR23","unstructured":"Hu, W., Miyato, T., Tokui, S., Matsumoto, E., Sugiyama, M.: Learning discrete representations via information maximizing self-augmented training. In: International Conference on Machine Learning (ICML) (2017)"},{"key":"26_CR24","unstructured":"Ioffe, S., Szegedy, C.: Batch normalization: accelerating deep network training by reducing internal covariate shift. In: International Conference on Machine Learning (ICML) (2015)"},{"key":"26_CR25","unstructured":"Iwasawa, Y., Matsuo, Y.: Test-time classifier adjustment module for model-agnostic domain generalization. In: Advances in Neural Information Processing Systems (NeurIPS) (2021)"},{"key":"26_CR26","doi-asserted-by":"crossref","unstructured":"Jain, H., Zepeda, J., P\u00e9rez, P., Gribonval, R.: Learning a complete image indexing pipeline. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2018)","DOI":"10.1109\/CVPR.2018.00518"},{"key":"26_CR27","unstructured":"Krause, A., Perona, P., Gomes, R.: Discriminative clustering by regularized information maximization. In: Advances in Neural Information Processing Systems (NeurIPS) (2010)"},{"key":"26_CR28","unstructured":"Krause, A., Perona, P., Gomes, R.: Discriminative clustering by regularized information maximization. In: Lafferty, J., Williams, C., Shawe-Taylor, J., Zemel, R., Culotta, A. (eds.) Advances in Neural Information Processing Systems (NeurIPS) (2010)"},{"key":"26_CR29","unstructured":"Krizhevsky, A., Hinton, G., et al.: Learning multiple layers of features from tiny images (2009)"},{"key":"26_CR30","unstructured":"Kundu, J.N., Venkat, N., Babu, R.V., et al.: Universal source-free domain adaptation. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2020)"},{"key":"26_CR31","doi-asserted-by":"crossref","unstructured":"Li, D., Yang, Y., Song, Y.Z., Hospedales, T.M.: Deeper, broader and artier domain generalization. In: International Conference on Computer Vision (ICCV) (2017)","DOI":"10.1109\/ICCV.2017.591"},{"key":"26_CR32","doi-asserted-by":"crossref","unstructured":"Li, D., Yang, Y., Song, Y.Z., Hospedales, T.M.: Learning to generalize: meta-learning for domain generalization. arXiv preprint arXiv:1710.03463 (2017)","DOI":"10.1609\/aaai.v32i1.11596"},{"key":"26_CR33","doi-asserted-by":"crossref","unstructured":"Li, D., Zhang, J., Yang, Y., Liu, C., Song, Y.Z., Hospedales, T.M.: Episodic training for domain generalization. In: International Conference on Computer Vision (ICCV) (2019)","DOI":"10.1109\/ICCV.2019.00153"},{"key":"26_CR34","doi-asserted-by":"crossref","unstructured":"Li, H., Jialin Pan, S., Wang, S., Kot, A.C.: Domain generalization with adversarial feature learning. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2018)","DOI":"10.1109\/CVPR.2018.00566"},{"key":"26_CR35","doi-asserted-by":"crossref","unstructured":"Li, L., et al.: Progressive domain expansion network for single domain generalization. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2021)","DOI":"10.1109\/CVPR46437.2021.00029"},{"key":"26_CR36","doi-asserted-by":"crossref","unstructured":"Li, R., Jiao, Q., Cao, W., Wong, H.S., Wu, S.: Model adaptation: unsupervised domain adaptation without source data. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2020)","DOI":"10.1109\/CVPR42600.2020.00966"},{"key":"26_CR37","doi-asserted-by":"crossref","unstructured":"Li, Y., et al.: Deep domain generalization via conditional invariant adversarial networks. In: European Conference on Computer Vision (ECCV) (2018)","DOI":"10.1007\/978-3-030-01267-0_38"},{"key":"26_CR38","unstructured":"Liang, J., Hu, D., Feng, J.: Do we really need to access the source data? source hypothesis transfer for unsupervised domain adaptation. In: International Conference on Machine Learning (ICML) (2020)"},{"key":"26_CR39","unstructured":"Liu, Y., Kothari, P., van Delft, B., Bellot-Gurlet, B., Mordan, T., Alahi, A.: TTT++: when does self-supervised test-time training fail or thrive? In: Advances in Neural Information Processing Systems (NeurIPS) (2021)"},{"key":"26_CR40","unstructured":"Long, M., Zhu, H., Wang, J., Jordan, M.I.: Unsupervised domain adaptation with residual transfer networks. In: Advances in Neural Information Processing Systems (NeurIPS) (2016)"},{"key":"26_CR41","unstructured":"Van der Maaten, L., Hinton, G.: Visualizing data using t-SNE. J. Mach. Learn. Res. (2008)"},{"key":"26_CR42","unstructured":"Mummadi, C.K., Hutmacher, R., Rambach, K., Levinkov, E., Brox, T., Metzen, J.H.: Test-time adaptation to distribution shift by confidence maximization and input transformation. arXiv preprint arXiv:2106.14999 (2021)"},{"key":"26_CR43","unstructured":"Nair, V., Hinton, G.E.: Rectified linear units improve restricted boltzmann machines. In: International Conference on Machine Learning (ICML) (2010)"},{"key":"26_CR44","doi-asserted-by":"crossref","unstructured":"Pan, X., Luo, P., Shi, J., Tang, X.: Two at once: enhancing learning and generalization capacities via IBN-Net. In: European Conference on Computer Vision (ECCV) (2018)","DOI":"10.1007\/978-3-030-01225-0_29"},{"key":"26_CR45","doi-asserted-by":"crossref","unstructured":"Rahman, M.M., Fookes, C., Baktashmotlagh, M., Sridharan, S.: Correlation-aware adversarial domain adaptation and generalization. Pattern Recogn. (2020)","DOI":"10.1016\/j.patcog.2019.107124"},{"key":"26_CR46","doi-asserted-by":"crossref","unstructured":"Saito, K., Watanabe, K., Ushiku, Y., Harada, T.: Maximum classifier discrepancy for unsupervised domain adaptation. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2018)","DOI":"10.1109\/CVPR.2018.00392"},{"key":"26_CR47","doi-asserted-by":"crossref","unstructured":"Seo, S., Suh, Y., Kim, D., Han, J., Han, B.: Learning to optimize domain specific normalization for domain generalization. arXiv preprint arXiv:1907.04275 (2019)","DOI":"10.1007\/978-3-030-58542-6_5"},{"key":"26_CR48","unstructured":"Shi, Y., Sha, F.: Information-theoretical learning of discriminative clusters for unsupervised domain adaptation. In: International Conference on Machine Learning (ICML) (2012)"},{"key":"26_CR49","unstructured":"Springenberg, J.T.: Unsupervised and semi-supervised learning with categorical generative adversarial networks. International Conference on Learning Representations (ICLR) (2016)"},{"key":"26_CR50","doi-asserted-by":"crossref","unstructured":"Su, J.C., Maji, S., Hariharan, B.: When does self-supervision improve few-shot learning? In: European Conference on Computer Vision (ECCV) (2020)","DOI":"10.1007\/978-3-030-58571-6_38"},{"key":"26_CR51","doi-asserted-by":"crossref","unstructured":"Sun, B., Saenko, K.: Deep coral: Correlation alignment for deep domain adaptation. In: European Conference on Computer Vision (ECCV) (2016)","DOI":"10.1007\/978-3-319-49409-8_35"},{"key":"26_CR52","unstructured":"Sun, Y., Wang, X., Liu, Z., Miller, J., Efros, A., Hardt, M.: Test-time training with self-supervision for generalization under distribution shifts. In: International Conference on Machine Learning (ICML) (2020)"},{"key":"26_CR53","doi-asserted-by":"crossref","unstructured":"Tzeng, E., Hoffman, J., Saenko, K., Darrell, T.: Adversarial discriminative domain adaptation. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2017)","DOI":"10.1109\/CVPR.2017.316"},{"key":"26_CR54","doi-asserted-by":"crossref","unstructured":"Vapnik, V.N.: An overview of statistical learning theory. IEEE Trans. Neural Netw. (1999)","DOI":"10.1109\/72.788640"},{"key":"26_CR55","doi-asserted-by":"crossref","unstructured":"Venkateswara, H., Eusebio, J., Chakraborty, S., Panchanathan, S.: Deep hashing network for unsupervised domain adaptation. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2017)","DOI":"10.1109\/CVPR.2017.572"},{"key":"26_CR56","unstructured":"Volpi, R., Namkoong, H., Sener, O., Duchi, J.C., Murino, V., Savarese, S.: Generalizing to unseen domains via adversarial data augmentation. In: Advances in Neural Information Processing systems 31 (2018)"},{"key":"26_CR57","doi-asserted-by":"crossref","unstructured":"Vu, T.H., Jain, H., Bucher, M., Cord, M., P\u00e9rez, P.: ADVENT: adversarial entropy minimization for domain adaptation in semantic segmentation. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2019)","DOI":"10.1109\/CVPR.2019.00262"},{"key":"26_CR58","unstructured":"Wang, D., Liu, S., Ebrahimi, S., Shelhamer, E., Darrell, T.: On-target adaptation. arXiv preprint arXiv:2109.01087 (2021)"},{"key":"26_CR59","unstructured":"Wang, D., Shelhamer, E., Liu, S., Olshausen, B., Darrell, T.: Tent: fully test-time adaptation by entropy minimization. In: International Conference on Learning Representations (ICLR) (2020)"},{"key":"26_CR60","doi-asserted-by":"crossref","unstructured":"Yang, S., Wang, Y., van de Weijer, J., Herranz, L., Jui, S.: Generalized source-free domain adaptation. In: International Conference on Computer Vision (ICCV) (2021)","DOI":"10.1109\/ICCV48922.2021.00885"},{"key":"26_CR61","doi-asserted-by":"crossref","unstructured":"Yeh, H.W., Yang, B., Yuen, P.C., Harada, T.: SoFA: source-data-free feature alignment for unsupervised domain adaptation. In: IEEE\/CVF Winter Conference on Applications of Computer Vision (WACV) (2021)","DOI":"10.1109\/WACV48630.2021.00052"},{"key":"26_CR62","unstructured":"Yosinski, J., Clune, J., Bengio, Y., Lipson, H.: How transferable are features in deep neural networks? In: Advances in Neural Information Processing Systems (NeurIPS) (2014)"},{"key":"26_CR63","unstructured":"You, F., Li, J., Zhao, Z.: Test-time batch statistics calibration for covariate shift. arXiv preprint arXiv:2110.04065 (2021)"},{"key":"26_CR64","unstructured":"Yu, T., Kumar, S., Gupta, A., Levine, S., Hausman, K., Finn, C.: Gradient surgery for multi-task learning. In: Advances in Neural Information Processing Systems (NeurIPS) (2020)"},{"key":"26_CR65","doi-asserted-by":"crossref","unstructured":"Zagoruyko, S., Komodakis, N.: Wide residual networks. In: British Machine Vision Conference (BMVC) (2016)","DOI":"10.5244\/C.30.87"},{"key":"26_CR66","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Borse, S., Cai, H., Porikli, F.: AuxAdapt: stable and efficient test-time adaptation for temporally consistent video semantic segmentation. In: IEEE\/CVF Winter Conference on Applications of Computer Vision (WACV) (2022)","DOI":"10.1109\/WACV51458.2022.00269"},{"key":"26_CR67","doi-asserted-by":"crossref","unstructured":"Zhou, K., Yang, Y., Hospedales, T., Xiang, T.: Learning to generate novel domains for domain generalization. In: European Conference on Computer Vision (ECCV) (2020)","DOI":"10.1007\/978-3-030-58517-4_33"}],"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\/978-3-031-19827-4_26","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,11,1]],"date-time":"2022-11-01T14:49:35Z","timestamp":1667314175000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-19827-4_26"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031198267","9783031198274"],"references-count":67,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-19827-4_26","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"2 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)"}}]}}