{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,7]],"date-time":"2026-05-07T21:54:19Z","timestamp":1778190859032,"version":"3.51.4"},"publisher-location":"Cham","reference-count":48,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031197833","type":"print"},{"value":"9783031197840","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\/978-3-031-19784-0_25","type":"book-chapter","created":{"date-parts":[[2022,10,30]],"date-time":"2022-10-30T14:02:50Z","timestamp":1667138570000},"page":"426-442","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":19,"title":["Improving GANs for\u00a0Long-Tailed Data Through Group Spectral Regularization"],"prefix":"10.1007","author":[{"given":"Harsh","family":"Rangwani","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Naman","family":"Jaswani","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tejan","family":"Karmali","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Varun","family":"Jampani","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"R. Venkatesh","family":"Babu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,10,31]]},"reference":[{"key":"25_CR1","unstructured":"Bansal, N., Chen, X., Wang, Z.: Can we gain more from orthogonality regularizations in training deep networks? In: Advances in Neural Information Processing Systems, vol. 31 (2018)"},{"key":"25_CR2","unstructured":"Brock, A., Donahue, J., Simonyan, K.: Large scale GAN training for high fidelity natural image synthesis. arXiv preprint arXiv:1809.11096 (2018)"},{"key":"25_CR3","unstructured":"Cao, K., Wei, C., Gaidon, A., Arechiga, N., Ma, T.: Learning imbalanced datasets with label-distribution-aware margin loss. In: Advances in Neural Information Processing Systems (2019)"},{"key":"25_CR4","doi-asserted-by":"crossref","unstructured":"Cui, Y., Jia, M., Lin, T.Y., Song, Y., Belongie, S.: Class-balanced loss based on effective number of samples. In: CVPR (2019)","DOI":"10.1109\/CVPR.2019.00949"},{"key":"25_CR5","unstructured":"De Vries, H., Strub, F., Mary, J., Larochelle, H., Pietquin, O., Courville, A.C.: Modulating early visual processing by language. In: Advances in Neural Information Processing Systems (2017)"},{"key":"25_CR6","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: 2009 IEEE Conference on Computer Vision and Pattern Recognition (2009)","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"25_CR7","unstructured":"Goodfellow, I., et al.: Generative adversarial nets. In: Advances in neural information processing systems (2014)"},{"key":"25_CR8","unstructured":"Gulrajani, I., Ahmed, F., Arjovsky, M., Dumoulin, V., Courville, A.C.: Improved training of Wasserstein GANs. In: Advances in neural information processing systems (2017)"},{"key":"25_CR9","doi-asserted-by":"crossref","unstructured":"Huang, L., Zhou, Y., Liu, L., Zhu, F., Shao, L.: Group whitening: balancing learning efficiency and representational capacity. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (2021)","DOI":"10.1109\/CVPR46437.2021.00939"},{"key":"25_CR10","unstructured":"iNaturalist: The inaturalist 2019 competition dataset. http:\/\/github.com\/visipedia\/inat_comp\/tree\/2019 (2019)"},{"key":"25_CR11","first-page":"21346","volume":"33","author":"G Jin","year":"2020","unstructured":"Jin, G., Yi, X., Zhang, L., Zhang, L., Schewe, S., Huang, X.: How does weight correlation affect generalisation ability of deep neural networks? Adv. Neural. Inf. Process. Syst. 33, 21346\u201321356 (2020)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"25_CR12","unstructured":"Kang, B., et al.: Decoupling representation and classifier for long-tailed recognition. In: International Conference on Learning Representations (2019)"},{"key":"25_CR13","unstructured":"Kang, M., Park, J.: Contrastive generative adversarial networks. arXiv preprint arXiv:2006.12681 (2020)"},{"key":"25_CR14","unstructured":"Karras, T., Aittala, M., Hellsten, J., Laine, S., Lehtinen, J., Aila, T.: Training generative adversarial networks with limited data. arXiv preprint arXiv:2006.06676 (2020)"},{"key":"25_CR15","unstructured":"Karras, T., Aittala, M., Hellsten, J., Laine, S., Lehtinen, J., Aila, T.: Training generative adversarial networks with limited data. In: Proceedings NeurIPS (2020)"},{"key":"25_CR16","unstructured":"Kavalerov, I., Czaja, W., Chellappa, R.: CGANs with multi-hinge loss. arXiv preprint arXiv:1912.04216 (2019)"},{"key":"25_CR17","doi-asserted-by":"crossref","unstructured":"Kolouri, S., Zou, Y., Rohde, G.K.: Sliced Wasserstein kernels for probability distributions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2016)","DOI":"10.1109\/CVPR.2016.568"},{"key":"25_CR18","unstructured":"Krizhevsky, A.: Learning multiple layers of features from tiny images. Technical Report (2009)"},{"key":"25_CR19","unstructured":"Kynk\u00e4\u00e4nniemi, T., Karras, T., Laine, S., Lehtinen, J., Aila, T.: Improved precision and recall metric for assessing generative models. In: Advances in Neural Information Processing Systems, vol. 32 (2019)"},{"key":"25_CR20","doi-asserted-by":"crossref","unstructured":"Ledig, C., et al.: Photo-realistic single image super-resolution using a generative adversarial network. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2017)","DOI":"10.1109\/CVPR.2017.19"},{"key":"25_CR21","doi-asserted-by":"crossref","unstructured":"Liu, K., Tang, W., Zhou, F., Qiu, G.: Spectral regularization for combating mode collapse in GANs. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 6382\u20136390 (2019)","DOI":"10.1109\/ICCV.2019.00648"},{"key":"25_CR22","doi-asserted-by":"crossref","unstructured":"Liu, M.Y., Huang, X., Yu, J., Wang, T.C., Mallya, A.: Generative adversarial networks for image and video synthesis: algorithms and applications. Proc. IEEE 109(5), 839\u2013862 (2021)","DOI":"10.1109\/JPROC.2021.3049196"},{"key":"25_CR23","doi-asserted-by":"crossref","unstructured":"Mao, Q., Lee, H.Y., Tseng, H.Y., Ma, S., Yang, M.H.: Mode seeking generative adversarial networks for diverse image synthesis. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2019)","DOI":"10.1109\/CVPR.2019.00152"},{"key":"25_CR24","doi-asserted-by":"crossref","unstructured":"Mao, X., Li, Q., Xie, H., Lau, R.Y., Wang, Z., Paul Smolley, S.: Least squares generative adversarial networks. In: Proceedings of the IEEE International Conference on Computer Vision (2017)","DOI":"10.1109\/ICCV.2017.304"},{"key":"25_CR25","unstructured":"Menon, A.K., Jayasumana, S., Rawat, A.S., Jain, H., Veit, A., Kumar, S.: Long-tail learning via logit adjustment. In: International Conference on Learning Representations (2021)"},{"key":"25_CR26","unstructured":"Miyato, T., Kataoka, T., Koyama, M., Yoshida, Y.: Spectral normalization for generative adversarial networks. arXiv preprint arXiv:1802.05957 (2018)"},{"key":"25_CR27","unstructured":"Miyato, T., Koyama, M.: cGANs with projection discriminator. In: International Conference on Learning Representations (2018)"},{"key":"25_CR28","doi-asserted-by":"crossref","unstructured":"Mullick, S.S., Datta, S., Das, S.: Generative adversarial minority oversampling. In: The IEEE International Conference on Computer Vision (ICCV) (2019)","DOI":"10.1109\/ICCV.2019.00178"},{"key":"25_CR29","unstructured":"Naeem, M.F., Oh, S.J., Uh, Y., Choi, Y., Yoo, J.: Reliable fidelity and diversity metrics for generative models (2020)"},{"key":"25_CR30","unstructured":"Odena, A., Olah, C., Shlens, J.: Conditional image synthesis with auxiliary classifier GANs. In: Proceedings of the 34th International Conference on Machine Learning-Volume, vol. 70 (2017)"},{"key":"25_CR31","unstructured":"Rangwani, H., Mopuri, K.R., Babu, R.V.: Class balancing GAN with a classifier in the loop. arXiv preprint arXiv:2106.09402 (2021)"},{"key":"25_CR32","unstructured":"Salimans, T., Goodfellow, I., Zaremba, W., Cheung, V., Radford, A., Chen, X.: improved techniques for training GANs. In: Advances in neural information processing systems (2016)"},{"key":"25_CR33","unstructured":"Santurkar, S., Schmidt, L., Madry, A.: A classification-based study of covariate shift in GAN distributions. In: International Conference on Machine Learning, pp. 4480\u20134489 (2018)"},{"issue":"7","key":"25_CR34","doi-asserted-by":"publisher","first-page":"1354","DOI":"10.1109\/TPAMI.2011.227","volume":"34","author":"Z Si","year":"2011","unstructured":"Si, Z., Zhu, S.C.: Learning hybrid image templates (HIT) by information projection. IEEE Trans. Pattern Anal. Mach. Intell. 34(7), 1354\u20131367 (2011)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"25_CR35","doi-asserted-by":"publisher","first-page":"1882","DOI":"10.1109\/TIP.2021.3049346","volume":"30","author":"NT Tran","year":"2021","unstructured":"Tran, N.T., Tran, V.H., Nguyen, N.B., Nguyen, T.K., Cheung, N.M.: On data augmentation for GAN training. IEEE Trans. Image Process. 30, 1882\u20131897 (2021)","journal-title":"IEEE Trans. Image Process."},{"key":"25_CR36","doi-asserted-by":"crossref","unstructured":"Tseng, H.Y., Jiang, L., Liu, C., Yang, M.H., Yang, W.: Regularizing generative adversarial networks under limited data. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (2021)","DOI":"10.1109\/CVPR46437.2021.00783"},{"key":"25_CR37","unstructured":"Vahdat, A., Kautz, J.: NVAE: a deep hierarchical variational autoencoder. In: Neural Information Processing Systems (NeurIPS) (2020)"},{"key":"25_CR38","unstructured":"Wang, Y.X., Ramanan, D., Hebert, M.: Learning to model the tail. In: Advances in Neural Information Processing Systems (2017)"},{"key":"25_CR39","first-page":"19099","volume":"33","author":"Z Wang","year":"2020","unstructured":"Wang, Z., Xiang, C., Zou, W., Xu, C.: MMA regularization: decorrelating weights of neural networks by maximizing the minimal angles. Adv. Neural. Inf. Process. Syst. 33, 19099\u201319110 (2020)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"25_CR40","doi-asserted-by":"crossref","unstructured":"Wu, Y., He, K.: Group normalization. In: Proceedings of the European conference on computer vision (ECCV) (2018)","DOI":"10.1007\/978-3-030-01261-8_1"},{"key":"25_CR41","unstructured":"Yang, Y., Xu, Z.: Rethinking the value of labels for improving class-imbalanced learning. In: NeurIPS (2020)"},{"key":"25_CR42","unstructured":"Yoshida, Y., Miyato, T.: Spectral norm regularization for improving the generalizability of deep learning. arXiv preprint arXiv:1705.10941 (2017)"},{"key":"25_CR43","unstructured":"Yu, F., Zhang, Y., Song, S., Seff, A., Xiao, J.: Lsun: Construction of a large-scale image dataset using deep learning with humans in the loop. CoRR abs\/1506.03365 (2015)"},{"key":"25_CR44","unstructured":"Zhang, H., Zhang, Z., Odena, A., Lee, H.: Consistency regularization for generative adversarial networks. arXiv preprint arXiv:1910.12027 (2019)"},{"key":"25_CR45","unstructured":"Zhao, S., Liu, Z., Lin, J., Zhu, J.Y., Han, S.: Differentiable augmentation for data-efficient GAN training. In: Advances in Neural Information Processing Systems, vol. 33 (2020)"},{"key":"25_CR46","doi-asserted-by":"crossref","unstructured":"Zhao, Z., Singh, S., Lee, H., Zhang, Z., Odena, A., Zhang, H.: Improved consistency regularization for GANs. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11033\u201311041 (2021)","DOI":"10.1609\/aaai.v35i12.17317"},{"key":"25_CR47","doi-asserted-by":"crossref","unstructured":"Zhou, B., Cui, Q., Wei, X.S., Chen, Z.M.: BBN: Bilateral-branch network with cumulative learning for long-tailed visual recognition, pp. 1\u20138 (2020)","DOI":"10.1109\/CVPR42600.2020.00974"},{"key":"25_CR48","doi-asserted-by":"crossref","unstructured":"Zhou, P., Xie, L., Ni, B., Geng, C., Tian, Q.: Omni-GAN: On the secrets of cGANs and beyond. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 14061\u201314071 (2021)","DOI":"10.1109\/ICCV48922.2021.01380"}],"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\/978-3-031-19784-0_25","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,13]],"date-time":"2024-03-13T19:36:07Z","timestamp":1710358567000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-19784-0_25"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031197833","9783031197840"],"references-count":48,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-19784-0_25","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":"31 October 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)"}}]}}