{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,4,24]],"date-time":"2025-04-24T04:03:57Z","timestamp":1745467437968,"version":"3.40.4"},"publisher-location":"Singapore","reference-count":33,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819660049","type":"print"},{"value":"9789819660056","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"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\/978-981-96-6005-6_16","type":"book-chapter","created":{"date-parts":[[2025,4,23]],"date-time":"2025-04-23T03:15:27Z","timestamp":1745378127000},"page":"223-234","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["TMMP: Efficient Continual Learning via\u00a0Mixture of\u00a0LoRA Experts and\u00a0Top Maximum Magnitude Parameter Fusion"],"prefix":"10.1007","author":[{"given":"Quynh-Trang Pham","family":"Thi","sequence":"first","affiliation":[]},{"given":"Le-Huy","family":"Pham","sequence":"additional","affiliation":[]},{"given":"Dinh-Dat","family":"Nguyen","sequence":"additional","affiliation":[]},{"given":"Tri-Thanh","family":"Nguyen","sequence":"additional","affiliation":[]},{"given":"Thanh Hai","family":"Dang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,4,21]]},"reference":[{"key":"16_CR1","doi-asserted-by":"crossref","unstructured":"Wang, L., Zhang, X., Hang, S., Jun, Z.: A comprehensive survey of continual learning: theory, method and application. IEEE Trans. Pattern Anal. Mach. Intell. (2024)","DOI":"10.1109\/TPAMI.2024.3367329"},{"key":"16_CR2","doi-asserted-by":"crossref","unstructured":"Yu, J. et al.: Boosting continual learning of vision-language models via mixture-of-experts adapters. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 23219-23230 (2024)","DOI":"10.1109\/CVPR52733.2024.02191"},{"key":"16_CR3","doi-asserted-by":"crossref","unstructured":"Liang, Y., Li, W.: InfLoRA: interference-free low-rank adaptation for continual learning. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 23638\u201323647 (2024)","DOI":"10.1109\/CVPR52733.2024.02231"},{"key":"16_CR4","unstructured":"Li, D., et al.: Mixlora: enhancing large language models fine-tuning with lora based mixture of experts. arXiv preprint arXiv:2404.15159 (2024)"},{"key":"16_CR5","unstructured":"Liu, Z., Ma, P., Wang, Y., Matusik, W., Max, T.: Kan 2.0: kolmogorov-arnold networks meet science. arXiv preprint arXiv:2408.10205 (2024)"},{"key":"16_CR6","doi-asserted-by":"crossref","unstructured":"Gao, P.:. Clip-adapter: better vision-language models with feature adapters. Int. J. Comput. Vis., 1-15 (2023)","DOI":"10.1007\/s11263-023-01891-x"},{"key":"16_CR7","doi-asserted-by":"crossref","unstructured":"Marczak, D., Twardowski, B., Trzci\u0144ski, T., Cygert, S.: Magmax: leveraging model merging for seamless continual learning. In: European Conference on Computer Vision, pp. 379-395, Cham. Springer Nature Switzerland (2024)","DOI":"10.1007\/978-3-031-73013-9_22"},{"key":"16_CR8","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. vol. 24, pp. 109-165. Academic Press (1989)","DOI":"10.1016\/S0079-7421(08)60536-8"},{"key":"16_CR9","unstructured":"Rusu, A.A., et al.: Progressive neural networks. arXiv preprint arXiv:1606.04671 (2016)"},{"key":"16_CR10","unstructured":"Krizhevsky, A., Hinton, G.: Learning multiple layers of features from tiny images (2009)"},{"key":"16_CR11","unstructured":"Ayub, A., Wagner, A.R.: Eec: learning to encode and regenerate images for continual learning. arXiv preprint arXiv:2101.04904 (2021)"},{"key":"16_CR12","unstructured":"Qin, C., Joty, S.: Lfpt5: a unified framework for lifelong few-shot language learning based on prompt tuning of t5. arXiv preprint arXiv:2110.07298 (2021)"},{"key":"16_CR13","unstructured":"Houlsby, N., et al.: Parameter-efficient transfer learning for NLP. In: International Conference on Machine Learning, pp. 2790-2799. PMLR (2019)"},{"key":"16_CR14","unstructured":"Hu, E.J., et al.: Lora: low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021)"},{"key":"16_CR15","doi-asserted-by":"crossref","unstructured":"Jia, M., et al.: Visual prompt tuning. In: European Conference on Computer Vision, pp. 709-727, Cham. Springer Nature Switzerland (2022)","DOI":"10.1007\/978-3-031-19827-4_41"},{"key":"16_CR16","doi-asserted-by":"crossref","unstructured":"Zheng, Z., Ma, M., Wang, K., Qin, Z., Yue, X., You, Y.: Preventing zero-shot transfer degradation in continual learning of vision-language models. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 19125-19136 (2023)","DOI":"10.1109\/ICCV51070.2023.01752"},{"key":"16_CR17","doi-asserted-by":"crossref","unstructured":"Kirkpatrick, J., et al.: Overcoming catastrophic forgetting in neural networks. Proc. Nat. Acad. Sci. 114, 3521-3526 (2017)","DOI":"10.1073\/pnas.1611835114"},{"key":"16_CR18","doi-asserted-by":"crossref","unstructured":"Castro, F., Mar\u0131n-Jimenez, M., Guil, N., Schmid, C., Alahari, K.: End-to-end incremental learning. In: Proceedings Of The European Conference On Computer Vision (ECCV), pp. 233-248 (2018)","DOI":"10.1007\/978-3-030-01258-8_15"},{"key":"16_CR19","doi-asserted-by":"crossref","unstructured":"Hou, S., Pan, X., Loy, C., Wang, Z., Lin, D.: Learning a unified classifier incrementally via rebalancing. In: Proceedings Of The IEEE\/CVF Conference On Computer Vision And Pattern Recognition, pp. 831-839 (2019)","DOI":"10.1109\/CVPR.2019.00092"},{"key":"16_CR20","doi-asserted-by":"crossref","unstructured":"Liu, Y., Parisot, S., Slabaugh, G., Jia, X., Leonardis, A., Tuytelaars, T.: More classifiers, less forgetting: a generic multi-classifier paradigm for incremental learning. In: Proceedings Of The European Conference On Computer Vision (ECCV), pp. 699-716 (2020)","DOI":"10.1007\/978-3-030-58574-7_42"},{"key":"16_CR21","doi-asserted-by":"crossref","unstructured":"Zhu, F., Zhang, X., Wang, C., Yin, F., Liu, C.: Prototype augmentation and self-supervision for incremental learning. In: Proceedings Of The IEEE\/CVF Conference On Computer Vision And Pattern Recognition, pp. 5871-5880 (2021)","DOI":"10.1109\/CVPR46437.2021.00581"},{"key":"16_CR22","doi-asserted-by":"crossref","unstructured":"Douillard, A., Ram\u00e9, A., Couairon, G., Cord, M.: Dytox: transformers for continual learning with dynamic token expansion. In: Proceedings of the IEEE\/CVF Conference on Computer Vision And Pattern Recognition, pp. 9285-9295 (2022)","DOI":"10.1109\/CVPR52688.2022.00907"},{"key":"16_CR23","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, 2935\u20132947 (2017)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"16_CR24","doi-asserted-by":"crossref","unstructured":"Rebuffi, S., Kolesnikov, A., Sperl, G., Lampert, C.: Icarl: incremental classifier and representation learning. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2001\u20132010 (2017)","DOI":"10.1109\/CVPR.2017.587"},{"key":"16_CR25","unstructured":"Ding, Y., Liu, L., Tian, C., Yang, J., Ding, H.: Don\u2019t stop learning: towards continual learning for the CLIP model. ArXiv Preprint ArXiv:2207.09248. (2022)"},{"key":"16_CR26","doi-asserted-by":"crossref","unstructured":"Wu, Y., et al.: Large scale incremental learning. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 374\u2013382 (2019)","DOI":"10.1109\/CVPR.2019.00046"},{"key":"16_CR27","doi-asserted-by":"crossref","unstructured":"Douillard, A., Cord, M., Ollion, C., Robert, T., Valle, E.: Podnet: pooled outputs distillation for small-tasks incremental learning. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 86\u2013102 (2020)","DOI":"10.1007\/978-3-030-58565-5_6"},{"key":"16_CR28","doi-asserted-by":"crossref","unstructured":"Yan, S., Xie, J., He, X.: Der: dynamically expandable representation for class incremental learning. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 3014\u20133023 (2021)","DOI":"10.1109\/CVPR46437.2021.00303"},{"key":"16_CR29","doi-asserted-by":"crossref","unstructured":"Hu, Z., Li, Y., Lyu, J., Gao, D., Vasconcelos, N.: Dense network expansion for class incremental learning. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 11858\u201311867 (2023)","DOI":"10.1109\/CVPR52729.2023.01141"},{"key":"16_CR30","unstructured":"Radford, A., et al.: Learning transferable visual models from natural language supervision. In: ICML (2021)"},{"key":"16_CR31","unstructured":"Dosovitskiy, A., et al.: An image is worth 16x16 words: transformers for image recognition at scale. In: ICLR (2021)"},{"key":"16_CR32","unstructured":"Yadav, P., Tam, D., Choshen, L., Raffel, C., Bansal, M.: TIES-merging: resolving interference when merging models. In: NeurIPS (2023)"},{"key":"16_CR33","unstructured":"Ortiz-Jim\u00e9nez, G., Favero, A., Frossard, P.: Task arithmetic in the tangent space: improved editing of pre-trained models. In: NeurIPS (2023)"}],"container-title":["Lecture Notes in Computer Science","Intelligent Information and Database Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-96-6005-6_16","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,4,23]],"date-time":"2025-04-23T03:15:55Z","timestamp":1745378155000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-96-6005-6_16"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"ISBN":["9789819660049","9789819660056"],"references-count":33,"URL":"https:\/\/doi.org\/10.1007\/978-981-96-6005-6_16","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025]]},"assertion":[{"value":"21 April 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ACIIDS","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Asian Conference on Intelligent Information and Database Systems","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Kitakyushu","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Japan","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 April 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"25 April 2025","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":"aciids2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/aciids.pwr.edu.pl\/2025\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}