{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,6]],"date-time":"2026-05-06T15:12:57Z","timestamp":1778080377306,"version":"3.51.4"},"publisher-location":"Cham","reference-count":68,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031200557","type":"print"},{"value":"9783031200564","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\/978-3-031-20056-4_42","type":"book-chapter","created":{"date-parts":[[2022,11,2]],"date-time":"2022-11-02T19:31:54Z","timestamp":1667417514000},"page":"725-742","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["Decoupled Adversarial Contrastive Learning for\u00a0Self-supervised Adversarial Robustness"],"prefix":"10.1007","author":[{"given":"Chaoning","family":"Zhang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kang","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chenshuang","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Axi","family":"Niu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiu","family":"Feng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chang D.","family":"Yoo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"In So","family":"Kweon","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,11,3]]},"reference":[{"key":"42_CR1","unstructured":"Athalye, A., Carlini, N., Wagner, D.: Obfuscated gradients give a false sense of security: circumventing defenses to adversarial examples. In: ICML (2018)"},{"key":"42_CR2","unstructured":"Bachman, P., Hjelm, R.D., Buchwalter, W.: Learning representations by maximizing mutual information across views. In: NeurIPS (2019)"},{"key":"42_CR3","unstructured":"Bardes, A., Ponce, J., LeCun, Y.: Vicreg: variance-invariance-covariance regularization for self-supervised learning. arXiv preprint arXiv:2105.04906 (2021)"},{"key":"42_CR4","doi-asserted-by":"crossref","unstructured":"Benz, P., Zhang, C., Imtiaz, T., Kweon, I.S.: Double targeted universal adversarial perturbations. In: ACCV (2020)","DOI":"10.1007\/978-3-030-69538-5_18"},{"key":"42_CR5","doi-asserted-by":"crossref","unstructured":"Benz, P., Zhang, C., Karjauv, A., Kweon, I.S.: Universal adversarial training with class-wise perturbations. In: ICME (2021)","DOI":"10.1109\/ICME51207.2021.9428419"},{"key":"42_CR6","doi-asserted-by":"crossref","unstructured":"Carlini, N., Wagner, D.: Adversarial examples are not easily detected. In: ACM Workshop on Artificial Intelligence and Security (2017)","DOI":"10.1145\/3128572.3140444"},{"key":"42_CR7","unstructured":"Carmon, Y., Raghunathan, A., Schmidt, L., Liang, P., Duchi, J.C.: Unlabeled data improves adversarial robustness. In: NeurIPS (2019)"},{"key":"42_CR8","unstructured":"Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. arXiv preprint arXiv:2006.09882 (2020)"},{"key":"42_CR9","doi-asserted-by":"crossref","unstructured":"Chen, T., Liu, S., Chang, S., Cheng, Y., Amini, L., Wang, Z.: Adversarial robustness: from self-supervised pre-training to fine-tuning. In: CVPR (2020)","DOI":"10.1109\/CVPR42600.2020.00078"},{"key":"42_CR10","unstructured":"Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: ICML (2020)"},{"key":"42_CR11","unstructured":"Chen, X., Fan, H., Girshick, R., He, K.: Improved baselines with momentum contrastive learning. arXiv preprint arXiv:2003.04297 (2020)"},{"key":"42_CR12","doi-asserted-by":"crossref","unstructured":"Chen, X., He, K.: Exploring simple siamese representation learning. In: CVPR (2021)","DOI":"10.1109\/CVPR46437.2021.01549"},{"key":"42_CR13","doi-asserted-by":"crossref","unstructured":"Chen, X., Xie, S., He, K.: An empirical study of training self-supervised vision transformers. In: ICCV (2021)","DOI":"10.1109\/ICCV48922.2021.00950"},{"key":"42_CR14","unstructured":"da Costa, V.G.T., Fini, E., Nabi, M., Sebe, N., Ricci, E.: Solo-learn: a library of self-supervised methods for visual representation learning. JMLR (2022)"},{"key":"42_CR15","unstructured":"Croce, F., Hein, M.: Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks. In: ICML (2020)"},{"key":"42_CR16","unstructured":"Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers) (2019)"},{"key":"42_CR17","unstructured":"El-Nouby, A., et al.: XCiT: cross-covariance image transformers. arXiv preprint arXiv:2106.09681 (2021)"},{"key":"42_CR18","unstructured":"Ermolov, A., Siarohin, A., Sangineto, E., Sebe, N.: Whitening for self-supervised representation learning. In: ICML. PMLR (2021)"},{"key":"42_CR19","unstructured":"Fan, L., Liu, S., Chen, P.Y., Zhang, G., Gan, C.: When does contrastive learning preserve adversarial robustness from pretraining to finetuning? In: NeurIPS (2021)"},{"key":"42_CR20","unstructured":"Gidaris, S., Singh, P., Komodakis, N.: Unsupervised representation learning by predicting image rotations. In: ICLR (2018)"},{"key":"42_CR21","unstructured":"Goodfellow, I.J., Shlens, J., Szegedy, C.: Explaining and harnessing adversarial examples. In: ICLR (2015)"},{"key":"42_CR22","unstructured":"Gowal, S., Huang, P.S., van den Oord, A., Mann, T., Kohli, P.: Self-supervised adversarial robustness for the low-label, high-data regime. In: ICLR (2021)"},{"key":"42_CR23","unstructured":"Gowal, S., Qin, C., Uesato, J., Mann, T., Kohli, P.: Uncovering the limits of adversarial training against norm-bounded adversarial examples. arXiv preprint arXiv:2010.03593 (2020)"},{"key":"42_CR24","unstructured":"Grill, J.B., et al.: Bootstrap your own latent-a new approach to self-supervised learning. In: Advances in Neural Information Processing Systems (2020)"},{"key":"42_CR25","doi-asserted-by":"crossref","unstructured":"He, K., Fan, H., Wu, Y., Xie, S., Girshick, R.: Momentum contrast for unsupervised visual representation learning. arXiv preprint arXiv:1911.05722 (2019)","DOI":"10.1109\/CVPR42600.2020.00975"},{"key":"42_CR26","doi-asserted-by":"crossref","unstructured":"He, K., Fan, H., Wu, Y., Xie, S., Girshick, R.: Momentum contrast for unsupervised visual representation learning. In: CVPR (2020)","DOI":"10.1109\/CVPR42600.2020.00975"},{"key":"42_CR27","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: CVPR (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"42_CR28","unstructured":"Henaff, O.: Data-efficient image recognition with contrastive predictive coding. In: ICML (2020)"},{"key":"42_CR29","unstructured":"Hjelm, R.D., et al.: Learning deep representations by mutual information estimation and maximization. arXiv preprint arXiv:1808.06670 (2018)"},{"key":"42_CR30","doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., Van Der Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks. In: CVPR (2017)","DOI":"10.1109\/CVPR.2017.243"},{"key":"42_CR31","unstructured":"Jiang, Z., Chen, T., Chen, T., Wang, Z.: Robust pre-training by adversarial contrastive learning. In: NeurIPS (2020)"},{"key":"42_CR32","unstructured":"Kim, M., Tack, J., Hwang, S.J.: Adversarial self-supervised contrastive learning. arXiv preprint arXiv:2006.07589 (2020)"},{"key":"42_CR33","unstructured":"Lan, Z., Chen, M., Goodman, S., Gimpel, K., Sharma, P., Soricut, R.: Albert: a lite bert for self-supervised learning of language representations. In: ICLR (2020)"},{"key":"42_CR34","unstructured":"Li, C., et al.: Efficient self-supervised vision transformers for representation learning. arXiv preprint arXiv:2106.09785 (2021)"},{"key":"42_CR35","unstructured":"Van der Maaten, L., Hinton, G.: Visualizing data using t-SNE. J. Mach. Learn. Res. (2008)"},{"key":"42_CR36","unstructured":"Madry, A., Makelov, A., Schmidt, L., Tsipras, D., Vladu, A.: Towards deep learning models resistant to adversarial attacks. In: ICLR (2018)"},{"key":"42_CR37","doi-asserted-by":"crossref","unstructured":"Moosavi-Dezfooli, S.M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR (2017)","DOI":"10.1109\/CVPR.2017.17"},{"key":"42_CR38","unstructured":"Najafi, A., Maeda, S.i., Koyama, M., Miyato, T.: Robustness to adversarial perturbations in learning from incomplete data. In: NeurIPS (2019)"},{"key":"42_CR39","doi-asserted-by":"crossref","unstructured":"Nie, P., Zhang, Y., Geng, X., Ramamurthy, A., Song, L., Jiang, D.: DC-BERT: decoupling question and document for efficient contextual encoding. In: Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval (2020)","DOI":"10.1145\/3397271.3401271"},{"key":"42_CR40","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"69","DOI":"10.1007\/978-3-319-46466-4_5","volume-title":"Computer Vision \u2013 ECCV 2016","author":"M Noroozi","year":"2016","unstructured":"Noroozi, M., Favaro, P.: Unsupervised learning of visual representations by solving jigsaw puzzles. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9910, pp. 69\u201384. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46466-4_5"},{"key":"42_CR41","unstructured":"Oord, A.V.D., Li, Y., Vinyals, O.: Representation learning with contrastive predictive coding. arXiv preprint arXiv:1807.03748 (2018)"},{"key":"42_CR42","unstructured":"Pang, T., Yang, X., Dong, Y., Su, H., Zhu, J.: Bag of tricks for adversarial training. arXiv preprint arXiv:2010.00467 (2020)"},{"key":"42_CR43","unstructured":"Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. OpenAI Blog (2019)"},{"key":"42_CR44","unstructured":"Rice, L., Wong, E., Kolter, Z.: Overfitting in adversarially robust deep learning. In: ICML (2020)"},{"key":"42_CR45","unstructured":"Richemond, P.H., et al.: Byol works even without batch statistics. arXiv preprint arXiv:2010.10241 (2020)"},{"key":"42_CR46","unstructured":"Schmidt, L., Santurkar, S., Tsipras, D., Talwar, K., Madry, A.: Adversarially robust generalization requires more data. In: NeurIPS (2018)"},{"key":"42_CR47","unstructured":"Su, W., Zhu, X., Cao, Y., Li, B., Lu, L., Wei, F., Dai, J.: VL-bert: pre-training of generic visual-linguistic representations. In: ICLR (2020)"},{"key":"42_CR48","unstructured":"Szegedy, C., et al.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013)"},{"key":"42_CR49","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"776","DOI":"10.1007\/978-3-030-58621-8_45","volume-title":"Computer Vision \u2013 ECCV 2020","author":"Y Tian","year":"2020","unstructured":"Tian, Y., Krishnan, D., Isola, P.: Contrastive multiview coding. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12356, pp. 776\u2013794. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58621-8_45"},{"key":"42_CR50","unstructured":"Uesato, J., Alayrac, J.B., Huang, P.S., Stanforth, R., Fawzi, A., Kohli, P.: Are labels required for improving adversarial robustness? In: NeurIPS (2019)"},{"key":"42_CR51","unstructured":"Wang, T., Isola, P.: Understanding contrastive representation learning through alignment and uniformity on the hypersphere. In: ICML (2020)"},{"key":"42_CR52","doi-asserted-by":"crossref","unstructured":"Wang, X., Zhang, R., Shen, C., Kong, T., Li, L.: Dense contrastive learning for self-supervised visual pre-training. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2021)","DOI":"10.1109\/CVPR46437.2021.00304"},{"key":"42_CR53","doi-asserted-by":"crossref","unstructured":"Wu, Z., Xiong, Y., Yu, S.X., Lin, D.: Unsupervised feature learning via non-parametric instance discrimination. In: CVPR (2018)","DOI":"10.1109\/CVPR.2018.00393"},{"key":"42_CR54","unstructured":"Xie, C., Tan, M., Gong, B., Yuille, A., Le, Q.V.: Smooth adversarial training. arXiv preprint arXiv:2006.14536 (2020)"},{"key":"42_CR55","unstructured":"Xie, C., Yuille, A.: Intriguing properties of adversarial training at scale. In: ICLR (2020)"},{"key":"42_CR56","unstructured":"Xu, C., Yang, M.: Adversarial momentum-contrastive pre-training. arXiv preprint arXiv:2012.13154 (2020)"},{"key":"42_CR57","doi-asserted-by":"crossref","unstructured":"Yeh, C.H., Hong, C.Y., Hsu, Y.C., Liu, T.L., Chen, Y., LeCun, Y.: Decoupled contrastive learning. arXiv preprint arXiv:2110.06848 (2021)","DOI":"10.1007\/978-3-031-19809-0_38"},{"key":"42_CR58","unstructured":"Zbontar, J., Jing, L., Misra, I., LeCun, Y., Deny, S.: Barlow twins: self-supervised learning via redundancy reduction. In: ICML (2021)"},{"key":"42_CR59","unstructured":"Zhai, R., et al.: Adversarially robust generalization just requires more unlabeled data. arXiv preprint arXiv:1906.00555 (2019)"},{"key":"42_CR60","doi-asserted-by":"crossref","unstructured":"Zhang, C., et al.: Resnet or densenet? Introducing dense shortcuts to resnet. In: WACV (2021)","DOI":"10.1109\/WACV48630.2021.00359"},{"key":"42_CR61","doi-asserted-by":"crossref","unstructured":"Zhang, C., Benz, P., Imtiaz, T., Kweon, I.S.: Understanding adversarial examples from the mutual influence of images and perturbations. In: CVPR (2020)","DOI":"10.1109\/CVPR42600.2020.01453"},{"key":"42_CR62","doi-asserted-by":"crossref","unstructured":"Zhang, C., Benz, P., Karjauv, A., Kweon, I.S.: Universal adversarial perturbations through the lens of deep steganography: towards a fourier perspective. In: AAAI (2021)","DOI":"10.1609\/aaai.v35i4.16441"},{"key":"42_CR63","unstructured":"Zhang, C., et al.: Revisiting residual networks with nonlinear shortcuts. In: BMVC (2019)"},{"key":"42_CR64","doi-asserted-by":"crossref","unstructured":"Zhang, C., Zhang, K., Pham, T.X., Yoo, C., Kweon, I.S.: Dual temperature helps contrastive learning without many negative samples: towards understanding and simplifying MoCo. In: CVPR (2022)","DOI":"10.1109\/CVPR52688.2022.01404"},{"key":"42_CR65","unstructured":"Zhang, C., Zhang, K., Zhang, C., Pham, T.X., Yoo, C.D., Kweon, I.S.: How does simsiam avoid collapse without negative samples? A unified understanding with self-supervised contrastive learning. In: ICLR (2022)"},{"key":"42_CR66","unstructured":"Zhang, H., Yu, Y., Jiao, J., Xing, E.P., Ghaoui, L.E., Jordan, M.I.: Theoretically principled trade-off between robustness and accuracy. In: ICML (2019)"},{"key":"42_CR67","unstructured":"Zhang, J., Han, B., Niu, G., Liu, T., Sugiyama, M.: Where is the bottleneck of adversarial learning with unlabeled data? arXiv preprint arXiv:1911.08696 (2019)"},{"key":"42_CR68","doi-asserted-by":"crossref","unstructured":"Zhuang, C., Zhai, A.L., Yamins, D.: Local aggregation for unsupervised learning of visual embeddings. In: ICCV (2019)","DOI":"10.1109\/ICCV.2019.00610"}],"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\/978-3-031-20056-4_42","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,7]],"date-time":"2024-10-07T03:28:08Z","timestamp":1728271688000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-20056-4_42"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031200557","9783031200564"],"references-count":68,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-20056-4_42","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":"3 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)"}}]}}