{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,6]],"date-time":"2026-05-06T15:58:45Z","timestamp":1778083125846,"version":"3.51.4"},"publisher-location":"Cham","reference-count":67,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030585648","type":"print"},{"value":"9783030585655","type":"electronic"}],"license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"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":[],"published-print":{"date-parts":[[2020]]},"DOI":"10.1007\/978-3-030-58565-5_46","type":"book-chapter","created":{"date-parts":[[2020,11,11]],"date-time":"2020-11-11T12:03:19Z","timestamp":1605096199000},"page":"777-795","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":54,"title":["Learning Latent Representations Across Multiple Data Domains Using Lifelong VAEGAN"],"prefix":"10.1007","author":[{"given":"Fei","family":"Ye","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Adrian G.","family":"Bors","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2020,11,12]]},"reference":[{"key":"46_CR1","unstructured":"Achille, A., et al.: Life-long disentangled representation learning with cross-domain latent homologies. In: Proceedings of the Advances in Neural Information Processing Systems (NIPS), pp. 9873\u20139883 (2018)"},{"key":"46_CR2","unstructured":"Aljundi, R., et al.: Online continual learning with maximal interfered retrieval. In: Proceedings of the Neural Information Processing Systems (NIPS). arXiv preprint arXiv:1908.04742 (2019)"},{"key":"46_CR3","doi-asserted-by":"crossref","unstructured":"Aljundi, R., Chakravarty, P., Tuytelaars, T.: Expert gate: lifelong learning with a network of experts. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3366\u20133375 (2017)","DOI":"10.1109\/CVPR.2017.753"},{"key":"46_CR4","unstructured":"Aljundi, R., Lin, M., Goujaud, B., Bengio, Y.: Gradient based sample selection for online continual learning. In: Proceedings of the Neural Information Processing Systems (NIPS). arXiv preprint arXiv:1903.08671 (2019)"},{"key":"46_CR5","unstructured":"Arjovsky, M., Chintala, S., Bottou, L.: Wasserstein generative adversarial networks. In: Proceedings of the International Conference on Machine Learning (ICML), pp. 214\u2013223 (2017)"},{"key":"46_CR6","doi-asserted-by":"crossref","unstructured":"Aubry, M., Maturana, D., Efros, A.A., Russell, B.C., Sivic, J.: Seeing 3D chairs: exemplar part-based 2D\u20133D alignment using a large dataset of CAD models. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3762\u20133769 (2014)","DOI":"10.1109\/CVPR.2014.487"},{"key":"46_CR7","unstructured":"Burgess, C.P., et al.: Understanding disentangling in $$\\beta $$-VAE. In: Proceedings of the NIPS Workshop on Learning Disentangled Representation. arXiv preprint arXiv:1804.03599 (2017)"},{"key":"46_CR8","unstructured":"Chaudhry, A., et al.: On tiny episodic memories in continual learning. arXiv preprint arXiv:1902.10486 (2019)"},{"key":"46_CR9","doi-asserted-by":"crossref","unstructured":"Chen, B.C., Chen, C.S., Hsu, W.H.: Cross-age reference coding for age-invariant face recognition and retrieval. In: Proceedings of the European Conference on Computer Vision (ECCV), vol. LNCS 8694, pp. 768\u2013783 (2014)","DOI":"10.1007\/978-3-319-10599-4_49"},{"key":"46_CR10","unstructured":"Chen, L., Dai, S., Pu, Y., Li, C., Su, Q., Carin, L.: Symmetric variational auto encoder and connections to adversarial learning. In: Proceedings of the International Conference on Artificial Intelligence and Statistics (AISTATS) 2018, vol. PMLR 84, pp. 661\u2013669 (2018)"},{"key":"46_CR11","unstructured":"Chen, L., Dai, S., Pu, Y., Li, C., Su, Q., Carin, L.: Symmetric variational auto encoder and connections to adversarial learning. arXiv preprint arXiv:1709.01846 (2017)"},{"key":"46_CR12","unstructured":"Chen, T.Q., Li, X., Grosse, R.B., Duvenaud, D.K.: Isolating sources of disentanglement in variational autoencoders. In: Proceedings of the Advances in Neural Information Processing Systems (NIPS), pp. 2615\u20132625 (2018)"},{"key":"46_CR13","doi-asserted-by":"crossref","unstructured":"Chen, Z., Ma, N., Liu, B.: Lifelong learning for sentiment classification. In: Proceedings of the Annual Meeting of the Association for Comparative Linguistics and International Joint Conference on Natural Language Processing, pp. 750\u2013756 (2015)","DOI":"10.3115\/v1\/P15-2123"},{"key":"46_CR14","doi-asserted-by":"crossref","unstructured":"Dai, W., Yang, Q., Xue, G.R., Yu, Y.: Boosting for transfer learning. In: Proceedings of the International Conference on Machine Learning (ICML), pp. 193\u2013200 (2007)","DOI":"10.1145\/1273496.1273521"},{"key":"46_CR15","unstructured":"Donahue, J., Kr\u00e4henb\u00fchl, P., Darrell, T.: Adversarial feature learning. In: Proceedings of the International Conference on Learning Representations (ICLR). arXiv preprint arXiv:1605.09782 (2017)"},{"key":"46_CR16","unstructured":"Dumoulin, V., et al.: Adversarially learned inference. In: Proceedings of the International Conference on Learning Representations (ICLR). arXiv preprint arXiv:1606.00704 (2017)"},{"key":"46_CR17","unstructured":"Dupont, E.: Learning disentangled joint continuous and discrete representations. In: Proceedings of the Advances in Neural Information Processing Systems (NIPS), pp. 710\u2013720 (2018)"},{"key":"46_CR18","doi-asserted-by":"crossref","unstructured":"Erhan, D., Szegedy, C., Toshev, A., Anguelov, D.: Scalable object detection using deep neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2147\u20132154 (2014)","DOI":"10.1109\/CVPR.2014.276"},{"issue":"46","key":"46_CR19","doi-asserted-by":"publisher","first-page":"17564","DOI":"10.1073\/pnas.0605184103","volume":"103","author":"J Fagot","year":"2006","unstructured":"Fagot, J., Cook, R.G.: Evidence for large long-term memory capacities in baboons and pigeons and its implications for learning and the evolution of cognition. Proc. National Acad. Sci. (PNAS) 103(46), 17564\u201317567 (2006)","journal-title":"Proc. National Acad. Sci. (PNAS)"},{"key":"46_CR20","unstructured":"Gao, S., Brekelmans, R., ver Steeg, G., Galstyan, A.: Auto-encoding total correlation explanation. In: Proceedings of the International Conference on Artificial Intelligence and Statistics (AISTATS) 2018, vol. PMLR 89, pp. 1157\u20131166 (2019)"},{"key":"46_CR21","unstructured":"Goodfellow, I., et al.: Generative adversarial nets. In: Proceedings of the Advances in Neural Information Processing Systems (NIPS), pp. 2672\u20132680 (2014)"},{"key":"46_CR22","unstructured":"Gulrajani, I., Ahmed, F., Arjovsky, M., Dumoulin, V., Courville, A.C.: Improved training of Wasserstein GANs. In: Proceedings of the Advances in Neural Information Processing Systems (NIPS), pp. 5767\u20135777 (2017)"},{"key":"46_CR23","unstructured":"Gumbel, E.J.: Statistical theory of extreme values and some practical applications: a series of lectures (1954)"},{"key":"46_CR24","unstructured":"Heusel, M., Ramsauer, H., Unterthiner, T., Nessler, B., Hochreiter, S.: GANs trained by a two time-scale update rule converge to a local Nash equilibrium. In: Proceedings of the Advances in Neural Information Processing Systems (NIPS), pp. 6626\u20136637 (2017)"},{"key":"46_CR25","unstructured":"Higgins, I., et al.: $$\\beta $$-VAE: learning basic visual concepts with a constrained variational framework. In: Proceedings of the International Conference on Learning Representations (ICLR) (2017)"},{"key":"46_CR26","unstructured":"Hinton, G., Vinyals, O., Dean, J.: Distilling the knowledge in a neural network. In: Proceedings of the NIPS Deep Learning Workshop. arXiv preprint arXiv:1503.02531 (2014)"},{"key":"46_CR27","unstructured":"Jang, E., Gu, S., Poole, B.: Categorical reparameterization with Gumbel-Softmax. In: Proceedings of the International Conference on Learning Representations (ICLR). arXiv preprint arXiv:1611.01144 (2017)"},{"key":"46_CR28","unstructured":"Jeong, Y., Song, H.O.: Learning discrete and continuous factors of data via alternating disentanglement. In: Proceedings of the International Conference on Machine Learning (ICML), vol. PMLR 97, pp. 3091\u20133099 (2019)"},{"key":"46_CR29","unstructured":"Jung, H., Ju, J., Jung, M., Kim, J.: Less-forgetting learning in deep neural networks. arXiv preprint arXiv:1607.00122 (2016)"},{"key":"46_CR30","unstructured":"Kim, H., Mnih, A.: Disentangling by factorising. In: Proceedings of the International Conference on Machine Learning (ICML), vol. PMLR 80, pp. 2649\u20132658 (2018)"},{"key":"46_CR31","unstructured":"Kingma, D.P., Mohamed, S., Rezende, D.J., Welling, M.: Semi-supervised learning with deep generative models. In: Proceedings of the Advances in Neural Information Processing Systems (NIPS), pp. 3581\u20133589 (2014)"},{"key":"46_CR32","unstructured":"Kingma, D.P., Welling, M.: Auto-encoding variational Bayes. arXiv preprint arXiv:1312.6114 (2013)"},{"issue":"13","key":"46_CR33","doi-asserted-by":"publisher","first-page":"3521","DOI":"10.1073\/pnas.1611835114","volume":"114","author":"J Kirkpatrick","year":"2017","unstructured":"Kirkpatrick, J., et al.: Overcoming catastrophic forgetting in neural networks. Proc. National Acad. Sci. (PNAS) 114(13), 3521\u20133526 (2017)","journal-title":"Proc. National Acad. Sci. (PNAS)"},{"key":"46_CR34","unstructured":"Krizhevsky, A., Hinton, G.: Learning multiple layers of features from tiny images. Technical report (2009)"},{"key":"46_CR35","unstructured":"Kumar, A., Sattigeri, P., Balakrishnan, A.: Variational inference of disentangled latent concepts from unlabeled observations. In: Proceedings of the International Conference on Learning Representations (ICLR). arXiv preprint arXiv:1711.00848 (2018)"},{"key":"46_CR36","unstructured":"Larsen, A.B.L., S\u00f8nderby, S.K., Larochelle, H., Winther, O.: Autoencoding beyond pixels using a learned similarity metric. In: Proceedings of the International Conference on Machine Learning (ICML), pp. 1558\u20131566 (2015)"},{"key":"46_CR37","unstructured":"Li, C., et al.: Alice: towards understanding adversarial learning for joint distribution matching. In: Proceedings of the Advances in Neural Information Processing Systems (NIPS), pp. 5495\u20135503 (2017)"},{"issue":"12","key":"46_CR38","doi-asserted-by":"publisher","first-page":"2935","DOI":"10.1109\/TPAMI.2017.2773081","volume":"40","author":"Z Li","year":"2017","unstructured":"Li, Z., Hoiem, D.: Learning without forgetting. IEEE Trans. Pattern Anal. Mach. Intell. 40(12), 2935\u20132947 (2017)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"46_CR39","doi-asserted-by":"crossref","unstructured":"Liu, Z., Luo, P., Wang, X., Tang, X.: Deep learning face attributes in the wild. In: Proceedings of the IEEE International Conference on Computer Vision (ICCV), pp. 3730\u20133738 (2015)","DOI":"10.1109\/ICCV.2015.425"},{"key":"46_CR40","unstructured":"Maddison, C.J., Mnih, A., Teh, Y.W.: The concrete distribution: a continuous relaxation of discrete random variables. In: Proceedings of the International Conference on Learning Representations (ICLR). arXiv preprint arXiv:1611.00712 (2016)"},{"key":"46_CR41","unstructured":"Maddison, C.J., Tarlow, D., Minka, T.: A* sampling. In: Proceedings of the Advances in Neural Information Processing Systems (NIPS), pp. 1\u201310 (2014)"},{"key":"46_CR42","unstructured":"Makhzani, A., Shlens, J., Jaitly, N., Goodfellow, I., Frey, B.: Adversarial autoencoders. In: Proceedings of the International Conference on Learning Representations (ICLR). arXiv preprint arXiv:1511.05644 (2016)"},{"key":"46_CR43","unstructured":"Mescheder, L., Nowozin, S., Geiger, A.: Adversarial variational bayes: unifying variational autoencoders and generative adversarial networks. In: Proceedings of the International Conference on Machine Learning (ICML), vol. PMLR 70, pp. 2391\u20132400(2017)"},{"key":"46_CR44","unstructured":"Narayanaswamy, S., et al.: Learning disentangled representations with semi-supervised deep generative models. In: Proceedings of the Advances in Neural Information Processing Systems (NIPS), pp. 5925\u20135935 (2017)"},{"key":"46_CR45","unstructured":"Netzer, Y., Wang, T., Coates, A., Bissacco, A., Wu, B., Ng, A.Y.: Reading digits in natural images with unsupervised feature learning. In: NIPS Workshop on Deep Learning and Unsupervised Feature Learning (2011)"},{"key":"46_CR46","unstructured":"Nowozin, S., Cseke, B., Tomioka, R.: $$f$$-GAN: training generative neural samplers using variational divergence minimization. In: Proceedings of the Advances in Neural Information Processing Systems (NIPS), pp. 271\u2013279 (2016)"},{"key":"46_CR47","doi-asserted-by":"publisher","first-page":"54","DOI":"10.1016\/j.neunet.2019.01.012","volume":"113","author":"GI Parisi","year":"2019","unstructured":"Parisi, G.I., Kemker, R., Part, J.L., Kanan, C., Wermter, S.: Continual lifelong learning with neural networks: a review. Neural Netw. 113, 54\u201371 (2019)","journal-title":"Neural Netw."},{"issue":"4","key":"46_CR48","doi-asserted-by":"publisher","first-page":"497","DOI":"10.1109\/5326.983933","volume":"31","author":"R Polikar","year":"2001","unstructured":"Polikar, R., Upda, L., Upda, S.S., Honavar, V.: Learn++: an incremental learning algorithm for supervised neural networks. IEEE Trans. Syst. Man Cybern. Part C 31(4), 497\u2013508 (2001)","journal-title":"IEEE Trans. Syst. Man Cybern. Part C"},{"key":"46_CR49","unstructured":"Pu, Y., et al.: Adversarial symmetric variational autoencoder. In: Proceedings of the Advances in Neural Information Processing Systems (NIPS), pp. 4333\u20134342 (2017)"},{"key":"46_CR50","unstructured":"Ramapuram, J., Gregorova, M., Kalousis, A.: Lifelong generative modeling. In: Proceedings of the International Conference on Learning Representations (ICLR). arXiv preprint arXiv:1705.09847 (2017)"},{"key":"46_CR51","doi-asserted-by":"crossref","unstructured":"Rannen, A., Aljundi, R., Blaschko, M., Tuytelaars, T.: Encoder based lifelong learning. In: Proceedings of the IEEE International Conference on Computer Vision (ICCV), pp. 1320\u20131328 (2017)","DOI":"10.1109\/ICCV.2017.148"},{"key":"46_CR52","unstructured":"Rao, D., Visin, F., Rusu, A.A., Teh, Y.W., Pascanu, R., Hadsell, R.: Continual unsupervised representation learning. In: Proceedings of the Neural Information Processing Systems (NIPS). arXiv preprint arXiv:1910.14481 (2019)"},{"key":"46_CR53","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"737","DOI":"10.1007\/978-3-319-71246-8_45","volume-title":"Machine Learning and Knowledge Discovery in Databases","author":"I Redko","year":"2017","unstructured":"Redko, I., Habrard, A., Sebban, M.: Theoretical analysis of domain adaptation with optimal transport. In: Ceci, M., Hollm\u00e9n, J., Todorovski, L., Vens, C., D\u017eeroski, S. (eds.) ECML PKDD 2017. LNCS (LNAI), vol. 10535, pp. 737\u2013753. Springer, Cham (2017). https:\/\/doi.org\/10.1007\/978-3-319-71246-8_45"},{"key":"46_CR54","doi-asserted-by":"publisher","first-page":"398","DOI":"10.1016\/j.asoc.2017.03.005","volume":"56","author":"B Ren","year":"2017","unstructured":"Ren, B., Wang, H., Li, J., Gao, H.: Life-long learning based on dynamic combination model. Appl. Soft Comput. 56, 398\u2013404 (2017)","journal-title":"Appl. Soft Comput."},{"key":"46_CR55","unstructured":"Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. In: Proceedings of the Advances in Neural Information Processing Systems (NIPS), pp. 91\u201399 (2015)"},{"key":"46_CR56","unstructured":"Rezende, D.J., Mohamed, S., Wierstra, D.: Stochastic backpropagation and approximate inference in deep generative models. In: Proceedings of the International Conference on Machine Learning (ICML), vol. PMLR 32, pp. 1278\u20131286 (2014)"},{"issue":"5","key":"46_CR57","doi-asserted-by":"publisher","first-page":"465","DOI":"10.1016\/0005-1098(78)90005-5","volume":"14","author":"J Rissanen","year":"1978","unstructured":"Rissanen, J.: Modeling by shortest data description. Automatica 14(5), 465\u2013471 (1978)","journal-title":"Automatica"},{"key":"46_CR58","unstructured":"Rusu, A.A., et al.: Progressive neural networks. arXiv preprint arXiv:1606.04671 (2016)"},{"key":"46_CR59","unstructured":"Salimans, T., Goodfellow, I., Zaremba, W., Cheung, V., Radford, A., Chen, X.: Improved techniques for training GANs. In: Proceedings of the Advances in Neural Information Processing Systems (NIPS), pp. 2234\u20132242 (2016)"},{"key":"46_CR60","unstructured":"Shin, H., Lee, J.K., Kim, J., Kim, J.: Continual learning with deep generative replay. In: Proceedings of the Advances in Neural Information Processing Systems (NIPS), pp. 2990\u20132999 (2017)"},{"key":"46_CR61","unstructured":"Srivastava, A., Valkov, L., Russell, C., Gutmann, M.U., Sutton, C.: VEEGAN: reducing mode collapse in GANs using implicit variational learning. In: Proceedings of the Advances in Neural Information Processing Systems (NIPS), pp. 3308\u20133318 (2017)"},{"key":"46_CR62","doi-asserted-by":"crossref","unstructured":"Tessler, C., Givony, S., Zahavy, T., Mankowitz, D.J., Mannor, S.: A deep hierarchical approach to lifelong learning in Minecraft. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp. 1553\u20131561 (2017)","DOI":"10.1609\/aaai.v31i1.10744"},{"key":"46_CR63","unstructured":"Wang, X., Zhang, R., Sun, Y., Qi, J.: KDGAN: knowledge distillation with generative adversarial networks. In: Proceedings of the Advances in Neural Information Processing Systems (NIPS), pp. 775\u2013786 (2018)"},{"key":"46_CR64","unstructured":"Wu, C., Herranz, L., Liu, X., van de Weijer, J., Raducanu, B.: Memory replay GANs: learning to generate new categories without forgetting. In: Proceedings of the Advances in Neural Information Processing Systems (NIPS), pp. 5962\u20135972 (2018)"},{"key":"46_CR65","unstructured":"Yoon, J., Yang, E., Lee, J., Hwang, S.J.: Lifelong learning with dynamically expandable networks. In: Proceedings of the International Conference on Learning Representations (ICLR). arXiv preprint arXiv:1708.01547 (2017)"},{"key":"46_CR66","doi-asserted-by":"crossref","unstructured":"Zhai, M., Chen, L., Tung, F., He, J., Nawhal, M., Mori, G.: Lifelong GAN: continual learning for conditional image generation. arXiv preprint arXiv:1907.10107 (2019)","DOI":"10.1109\/ICCV.2019.00285"},{"key":"46_CR67","doi-asserted-by":"crossref","unstructured":"Zhai, M., Chen, L., Tung, F., He, J., Nawhal, M., Mori, G.: Lifelong GAN: continual learning for conditional image generation. In: Proceedings of the IEEE International Conference on Computer Vision (ICCV), pp. 2759\u20132768 (2019)","DOI":"10.1109\/ICCV.2019.00285"}],"container-title":["Lecture Notes in Computer Science","Computer Vision \u2013 ECCV 2020"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-58565-5_46","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,11]],"date-time":"2024-11-11T00:14:27Z","timestamp":1731284067000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-58565-5_46"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030585648","9783030585655"],"references-count":67,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-58565-5_46","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020]]},"assertion":[{"value":"12 November 2020","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":"Glasgow","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"United Kingdom","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2020","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 August 2020","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"28 August 2020","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"16","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"eccv2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/eccv2020.eu\/","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":"OpenReview","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"5025","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":"1360","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":"27% - 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","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":"7","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)"}},{"value":"The conference was held virtually due to the COVID-19 pandemic.","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}