{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,20]],"date-time":"2026-10-20T16:45:50Z","timestamp":1792514750199,"version":"4.5.0"},"publisher-location":"Cham","reference-count":53,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031730122","type":"print"},{"value":"9783031730139","type":"electronic"}],"license":[{"start":{"date-parts":[[2024,11,27]],"date-time":"2024-11-27T00:00:00Z","timestamp":1732665600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,11,27]],"date-time":"2024-11-27T00:00:00Z","timestamp":1732665600000},"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":[[2025]]},"DOI":"10.1007\/978-3-031-73013-9_22","type":"book-chapter","created":{"date-parts":[[2024,11,26]],"date-time":"2024-11-26T07:47:45Z","timestamp":1732607265000},"page":"379-395","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":26,"title":["MAGMAX: Leveraging Model Merging for\u00a0Seamless Continual Learning"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6352-9134","authenticated-orcid":false,"given":"Daniel","family":"Marczak","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2117-8679","authenticated-orcid":false,"given":"Bart\u0142omiej","family":"Twardowski","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1486-8906","authenticated-orcid":false,"given":"Tomasz","family":"Trzci\u0144ski","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4763-8381","authenticated-orcid":false,"given":"Sebastian","family":"Cygert","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,11,27]]},"reference":[{"key":"22_CR1","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"213","DOI":"10.1007\/978-3-030-58452-8_13","volume-title":"Computer Vision \u2013 ECCV 2020","author":"N Carion","year":"2020","unstructured":"Carion, N., Massa, F., Synnaeve, G., Usunier, N., Kirillov, A., Zagoruyko, S.: End-to-end object detection with transformers. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12346, pp. 213\u2013229. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58452-8_13"},{"key":"22_CR2","doi-asserted-by":"crossref","unstructured":"Caron, M., Touvron, H., Misra, I., Jegou, H., Mairal, J., Bojanowski, P., Joulin, A.: Emerging properties in self-supervised vision transformers. In: ICCV (2021)","DOI":"10.1109\/ICCV48922.2021.00951"},{"key":"22_CR3","doi-asserted-by":"crossref","unstructured":"Cheng, G., Han, J., Lu, X.: Remote sensing image scene classification: benchmark and state of the art. In: Proceedings of the IEEE (2017)","DOI":"10.1109\/JPROC.2017.2675998"},{"key":"22_CR4","doi-asserted-by":"crossref","unstructured":"Cimpoi, M., Maji, S., Kokkinos, I., Mohamed, S., Vedaldi, A.: Describing textures in the wild. In: CVPR (2014)","DOI":"10.1109\/CVPR.2014.461"},{"key":"22_CR5","unstructured":"Dosovitskiy, A., et al.: An image is worth 16x16 words: transformers for image recognition at scale. In: ICLR (2021)"},{"key":"22_CR6","unstructured":"Frankle, J., Dziugaite, G.K., Roy, D.M., Carbin, M.: Linear mode connectivity and the lottery ticket hypothesis. In: ICML (2020)"},{"key":"22_CR7","doi-asserted-by":"crossref","unstructured":"French, R.M.: Catastrophic forgetting in connectionist networks. Trends Cogn. Sci. (1999)","DOI":"10.1016\/S1364-6613(99)01294-2"},{"key":"22_CR8","unstructured":"Guo, Y., Yao, A., Chen, Y.: Dynamic network surgery for efficient DNNs. NeurIPS (2016)"},{"key":"22_CR9","unstructured":"Han, S., Pool, J., Tran, J., Dally, W.J.: Learning both weights and connections for efficient neural network. In: NeurIPS (2015)"},{"key":"22_CR10","doi-asserted-by":"crossref","unstructured":"Helber, P., Bischke, B., Dengel, A., Borth, D.: Eurosat: A novel dataset and deep learning benchmark for land use and land cover classification. J. Sel. Top. Appl. Earth Obs. Remote Sens. (2019)","DOI":"10.1109\/JSTARS.2019.2918242"},{"key":"22_CR11","doi-asserted-by":"crossref","unstructured":"Hendrycks, D., et al.: The many faces of robustness: a critical analysis of out-of-distribution generalization. In: ICCV (2020)","DOI":"10.1109\/ICCV48922.2021.00823"},{"key":"22_CR12","doi-asserted-by":"crossref","unstructured":"Hou, S., Pan, X., Loy, C.C., Wang, Z., Lin, D.: Learning a unified classifier incrementally via rebalancing. In: CVPR (2019)","DOI":"10.1109\/CVPR.2019.00092"},{"key":"22_CR13","unstructured":"Ilharco, G., Ribeiro, M.T., Wortsman, M., Schmidt, L., Hajishirzi, H., Farhadi, A.: Editing models with task arithmetic. In: ICLR (2023)"},{"key":"22_CR14","unstructured":"Ilharco, G., et al.: Patching open-vocabulary models by interpolating weights. In: NeurIPS (2022)"},{"key":"22_CR15","unstructured":"Janson, P., Zhang, W., Aljundi, R., Elhoseiny, M.: A simple baseline that questions the use of pretrained-models in continual learning. In: NeurIPS 2022 Workshop on Distribution Shifts: Connecting Methods and Applications"},{"key":"22_CR16","doi-asserted-by":"crossref","unstructured":"Kim, D., Han, B.: On the stability-plasticity dilemma of class-incremental learning. In: CVPR (2023)","DOI":"10.1109\/CVPR52729.2023.01934"},{"key":"22_CR17","doi-asserted-by":"crossref","unstructured":"Kirillov, A., et al.: Segment anything. In: ICCV (2023)","DOI":"10.1109\/ICCV51070.2023.00371"},{"key":"22_CR18","doi-asserted-by":"crossref","unstructured":"Kirkpatrick, J., Pascanu, R., Rabinowitz, N., Veness, J., Desjardins, G., Rusu, A.A., Milan, K., Quan, J., Ramalho, T., Grabska-Barwinska, A., et\u00a0al.: Overcoming catastrophic forgetting in neural networks. PNAS (2017)","DOI":"10.1073\/pnas.1611835114"},{"key":"22_CR19","doi-asserted-by":"crossref","unstructured":"Krause, J., Stark, M., Deng, J., Fei-Fei, L.: 3D object representations for fine-grained categorization. In: ICCV Workshops (2013)","DOI":"10.1109\/ICCVW.2013.77"},{"key":"22_CR20","unstructured":"Krizhevsky, A., Hinton, G., et\u00a0al.: Learning multiple layers of features from tiny images. University of Toronto (2009)"},{"key":"22_CR21","unstructured":"Lange, M.D., et al.: A continual learning survey: defying forgetting in classification tasks. IEEE TPAMI (2019)"},{"key":"22_CR22","unstructured":"LeCun, Y.: The MNIST database of handwritten digits (1998)"},{"key":"22_CR23","doi-asserted-by":"crossref","unstructured":"Li, Z., Hoiem, D.: Learning without forgetting. IEEE TPAMI (2018)","DOI":"10.1109\/TPAMI.2017.2773081"},{"key":"22_CR24","doi-asserted-by":"crossref","unstructured":"Marouf, I.E., Roy, S., Tartaglione, E., Lathuili\u00e8re, S.: Weighted ensemble models are strong continual learners. arXiv preprint arXiv: 2312.08977 (2023)","DOI":"10.1007\/978-3-031-73209-6_18"},{"key":"22_CR25","doi-asserted-by":"crossref","unstructured":"Masana, M., Liu, X., Twardowski, B., Menta, M., Bagdanov, A.D., van\u00a0de Weijer, J.: Class-incremental learning: survey and perfoxrmance evaluation on image classification. IEEE TPAMI (2023)","DOI":"10.1109\/TPAMI.2022.3213473"},{"key":"22_CR26","unstructured":"Matena, M., Raffel, C.: Merging models with fisher-weighted averaging. In: NeurIPS (2021)"},{"key":"22_CR27","doi-asserted-by":"crossref","unstructured":"McCloskey, M., Cohen, N.J.: Catastrophic interference in connectionist networks: The sequential learning problem. In: Psychology of Learning and Motivation (1989)","DOI":"10.1016\/S0079-7421(08)60536-8"},{"key":"22_CR28","unstructured":"Netzer, Y., Wang, T., Coates, A., Bissacco, A., Wu, B., Ng, A.Y.: Reading digits in natural images with unsupervised feature learning. In: NeurIPS Workshops (2011)"},{"key":"22_CR29","unstructured":"Ortiz-Jim\u00e9nez, G., Favero, A., Frossard, P.: Task arithmetic in the tangent space: improved editing of pre-trained models. In: NeurIPS (2023)"},{"key":"22_CR30","doi-asserted-by":"crossref","unstructured":"Peng, X., Bai, Q., Xia, X., Huang, Z., Saenko, K., Wang, B.: Moment matching for multi-source domain adaptation. In: ICCV (2019)","DOI":"10.1109\/ICCV.2019.00149"},{"key":"22_CR31","doi-asserted-by":"crossref","unstructured":"Petit, G., Popescu, A., Schindler, H., Picard, D., Delezoide, B.: FeTriL: feature translation for exemplar-free class-incremental learning. In: WACV (2023)","DOI":"10.1109\/WACV56688.2023.00390"},{"key":"22_CR32","unstructured":"Radford, A., et al.: Learning transferable visual models from natural language supervision. In: ICML (2021)"},{"key":"22_CR33","unstructured":"Rusu, A.A., et al.: Progressive neural networks. CoRR (2016)"},{"key":"22_CR34","unstructured":"Rype\u015b\u0107, G., Cygert, S., Khan, V., Trzci\u0144ski, T., Zieli\u0144ski, B., Twardowski, B.: Divide and not forget: Ensemble of selectively trained experts in continual learning. In: ICLR (2024)"},{"key":"22_CR35","unstructured":"Shah, V., et al.: ZipLora: any subject in any style by effectively merging loras. In: arXiv preprint arxiv:2311.13600"},{"key":"22_CR36","unstructured":"Singh, S.P., Jaggi, M.: Model fusion via optimal transport. In: NeurIPS (2020)"},{"key":"22_CR37","doi-asserted-by":"crossref","unstructured":"Smith, J.S., et al.: Coda-prompt: continual decomposed attention-based prompting for rehearsal-free continual learning. In: CVPR (2023)","DOI":"10.1109\/CVPR52729.2023.01146"},{"key":"22_CR38","doi-asserted-by":"crossref","unstructured":"Stallkamp, J., Schlipsing, M., Salmen, J., Igel, C.: The German traffic sign recognition benchmark: a multi-class classification competition. In: IJCNN (2011)","DOI":"10.1109\/IJCNN.2011.6033395"},{"key":"22_CR39","doi-asserted-by":"crossref","unstructured":"van\u00a0de Ven, G., Tuytelaars, T., Tolias, A.: Three types of incremental learning. Nat. Mach. Intell. (2022)","DOI":"10.1038\/s42256-022-00568-3"},{"key":"22_CR40","unstructured":"Wah, C., Branson, S., Welinder, P., Perona, P., Belongie, S.: The caltech-UCSD birds-200-2011 dataset (2011)"},{"key":"22_CR41","doi-asserted-by":"crossref","unstructured":"Wang, C.Y., Bochkovskiy, A., Liao, H.: Yolov7: trainable bag-of-freebies sets new state-of-the-art for real-time object detectors. In: CVPR (2022)","DOI":"10.1109\/CVPR52729.2023.00721"},{"key":"22_CR42","series-title":"LNCS","doi-asserted-by":"publisher","first-page":"398","DOI":"10.1007\/978-3-031-19806-9_23","volume-title":"ECCV 2022","author":"F Wang","year":"2022","unstructured":"Wang, F., Zhou, D., Ye, H., Zhan, D.: FOSTER: feature boosting and compression for class-incremental learning. In: Avidan, S., Brostow, G., Ciss\u00e9, M., Farinella, G.M., Hassner, T. (eds.) ECCV 2022. LNCS, vol. 13685, pp. 398\u2013414. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-031-19806-9_23"},{"key":"22_CR43","doi-asserted-by":"crossref","unstructured":"Wang, Z., et al.: Learning to prompt for continual learning. In: CVPR (2022)","DOI":"10.1109\/CVPR52688.2022.00024"},{"key":"22_CR44","unstructured":"Wortsman, M., et\u00a0al.: Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference time. In: ICML (2022)"},{"key":"22_CR45","doi-asserted-by":"crossref","unstructured":"Wortsman, M., et al.: Robust fine-tuning of zero-shot models. In: CVPR (2022)","DOI":"10.1109\/CVPR52688.2022.00780"},{"key":"22_CR46","unstructured":"Xiao, J., Ehinger, K.A., Hays, J., Torralba, A., Oliva, A.: Sun database: exploring a large collection of scene categories. IN: IJCV (2016)"},{"key":"22_CR47","unstructured":"Yadav, P., Tam, D., Choshen, L., Raffel, C., Bansal, M.: TIES-merging: resolving interference when merging models. In: NeurIPS (2023)"},{"key":"22_CR48","doi-asserted-by":"crossref","unstructured":"Yan, S., Xie, J., He, X.: DER: dynamically expandable representation for class incremental learning. In: CVPR (2021)","DOI":"10.1109\/CVPR46437.2021.00303"},{"key":"22_CR49","unstructured":"Yoon, J., Yang, E., Lee, J., Hwang, S.J.: Lifelong learning with dynamically expandable networks. In: ICLR (2018)"},{"key":"22_CR50","doi-asserted-by":"crossref","unstructured":"Zhai, X., Mustafa, B., Kolesnikov, A., Beyer, L.: Sigmoid loss for language image pre-training. In: ICCV (2023)","DOI":"10.1109\/ICCV51070.2023.01100"},{"key":"22_CR51","doi-asserted-by":"crossref","unstructured":"Zhang, G., Wang, L., Kang, G., Chen, L., Wei, Y.: SLCA: slow learner with classifier alignment for continual learning on a pre-trained model. In: ICCV (2023)","DOI":"10.1109\/ICCV51070.2023.01754"},{"key":"22_CR52","doi-asserted-by":"crossref","unstructured":"Zhao, B., Xiao, X., Gan, G., Zhang, B., Xia, S.: Maintaining discrimination and fairness in class incremental learning. In: CVPR (2020)","DOI":"10.1109\/CVPR42600.2020.01322"},{"key":"22_CR53","doi-asserted-by":"crossref","unstructured":"Zhu, K., Zhai, W., Cao, Y., Luo, J., Zha, Z.J.: Self-sustaining representation expansion for non-exemplar class-incremental learning. In: CVPR (2022)","DOI":"10.1109\/CVPR52688.2022.00908"}],"container-title":["Lecture Notes in Computer Science","Computer Vision \u2013 ECCV 2024"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-73013-9_22","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,26]],"date-time":"2024-11-26T08:32:16Z","timestamp":1732609936000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-73013-9_22"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,11,27]]},"ISBN":["9783031730122","9783031730139"],"references-count":53,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-73013-9_22","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,11,27]]},"assertion":[{"value":"27 November 2024","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":"Milan","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Italy","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29 September 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4 October 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"eccv2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/eccv2024.ecva.net\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}