{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,1]],"date-time":"2026-02-01T03:58:11Z","timestamp":1769918291795,"version":"3.49.0"},"publisher-location":"New York, NY, USA","reference-count":87,"publisher":"ACM","license":[{"start":{"date-parts":[[2024,8,24]],"date-time":"2024-08-24T00:00:00Z","timestamp":1724457600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2024,8,25]]},"DOI":"10.1145\/3637528.3671581","type":"proceedings-article","created":{"date-parts":[[2024,8,25]],"date-time":"2024-08-25T04:54:55Z","timestamp":1724561695000},"page":"5613-5624","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":8,"title":["Class-incremental Learning for Time Series: Benchmark and Evaluation"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-3170-7075","authenticated-orcid":false,"given":"Zhongzheng","family":"Qiao","sequence":"first","affiliation":[{"name":"IGP-ERI@N, NTU & I2R, A*STAR, Singapore, Singapore"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6416-5328","authenticated-orcid":false,"given":"Quang","family":"Pham","sequence":"additional","affiliation":[{"name":"I2R, A*STAR, Singapore, Singapore"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-4069-9679","authenticated-orcid":false,"given":"Zhen","family":"Cao","sequence":"additional","affiliation":[{"name":"I2R, A*STAR, Singapore, Singapore"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-6794-6964","authenticated-orcid":false,"given":"Hoang H.","family":"Le","sequence":"additional","affiliation":[{"name":"Ho Chi Minh University of Science, Vietnam National University, Ho Chi Minh City, Vietnam"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0901-5105","authenticated-orcid":false,"given":"P. N.","family":"Suganthan","sequence":"additional","affiliation":[{"name":"Qatar University, Dohar, Qatar"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9104-2315","authenticated-orcid":false,"given":"Xudong","family":"Jiang","sequence":"additional","affiliation":[{"name":"School of Electrical and Electronic Engineering, NTU, Singapore, Singapore"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1534-2989","authenticated-orcid":false,"given":"Savitha","family":"Ramasamy","sequence":"additional","affiliation":[{"name":"I2R, A*STAR & CNRS@CREATE, Singapore, Singapore"}]}],"member":"320","published-online":{"date-parts":[[2024,8,24]]},"reference":[{"key":"e_1_3_2_2_1_1","volume-title":"Generative Time-series Modeling with Fourier Flows. In International Conference on Learning Representations.","author":"Alaa Ahmed","unstructured":"Ahmed Alaa, Alex James Chan, and Mihaela van der Schaar. 2021. Generative Time-series Modeling with Fourier Flows. In International Conference on Learning Representations."},{"key":"e_1_3_2_2_2_1","doi-asserted-by":"crossref","unstructured":"Rahaf Aljundi Francesca Babiloni Mohamed Elhoseiny Marcus Rohrbach and Tinne Tuytelaars. 2018. Memory aware synapses: Learning what (not) to forget. In ECCV. 139--154.","DOI":"10.1007\/978-3-030-01219-9_9"},{"key":"e_1_3_2_2_3_1","doi-asserted-by":"crossref","unstructured":"Rahaf Aljundi Punarjay Chakravarty and Tinne Tuytelaars. 2017. Expert gate: Lifelong learning with a network of experts. In CVPR. 3366--3375.","DOI":"10.1109\/CVPR.2017.753"},{"key":"e_1_3_2_2_4_1","doi-asserted-by":"publisher","DOI":"10.5555\/1881331.1881338"},{"key":"e_1_3_2_2_5_1","doi-asserted-by":"publisher","DOI":"10.1109\/BHI56158.2022.9926878"},{"key":"e_1_3_2_2_6_1","volume-title":"Jamie Ryan Kiros, and Geoffrey E Hinton","author":"Ba Jimmy Lei","year":"2016","unstructured":"Jimmy Lei Ba, Jamie Ryan Kiros, and Geoffrey E Hinton. 2016. Layer normalization. arXiv preprint arXiv:1607.06450 (2016)."},{"key":"e_1_3_2_2_7_1","first-page":"15920","article-title":"Dark experience for general continual learning: a strong, simple baseline","volume":"33","author":"Buzzega Pietro","year":"2020","unstructured":"Pietro Buzzega, Matteo Boschini, Angelo Porrello, Davide Abati, and Simone Calderara. 2020. Dark experience for general continual learning: a strong, simple baseline. NIPS, Vol. 33 (2020), 15920--15930.","journal-title":"NIPS"},{"key":"e_1_3_2_2_8_1","first-page":"1","article-title":"Avalanche: A PyTorch Library for Deep Continual Learning","volume":"24","author":"Carta Antonio","year":"2023","unstructured":"Antonio Carta, Lorenzo Pellegrini, Andrea Cossu, Hamed Hemati, and Vincenzo Lomonaco. 2023. Avalanche: A PyTorch Library for Deep Continual Learning. Journal of Machine Learning Research, Vol. 24, 363 (2023), 1--6. http:\/\/jmlr.org\/papers\/v24\/23-0130.html","journal-title":"Journal of Machine Learning Research"},{"key":"e_1_3_2_2_9_1","unstructured":"Arslan Chaudhry Marc'Aurelio Ranzato Marcus Rohrbach and Mohamed Elhoseiny. 2018. Efficient Lifelong Learning with A-GEM. In ICLR."},{"key":"e_1_3_2_2_10_1","volume-title":"Workshop on Multi-Task and Lifelong Reinforcement Learning.","author":"Chaudhry Arslan","year":"2019","unstructured":"Arslan Chaudhry, Marcus Rohrbach, Mohamed Elhoseiny, Thalaiyasingam Ajanthan, P Dokania, P Torr, and M Ranzato. 2019. Continual learning with tiny episodic memories. In Workshop on Multi-Task and Lifelong Reinforcement Learning."},{"key":"e_1_3_2_2_11_1","doi-asserted-by":"publisher","DOI":"10.1145\/3432203"},{"key":"e_1_3_2_2_12_1","unstructured":"Tianqi Chen Ian Goodfellow and Jonathon Shlens. 2016. Net2Net: Accelerating Learning via Knowledge Transfer. arxiv: 1511.05641 [cs.LG]"},{"key":"e_1_3_2_2_13_1","volume-title":"Proceedings of The First AAAI Bridge Program on Continual Causality (Proceedings of Machine Learning Research","volume":"70","author":"Churamani Nikhil","year":"2023","unstructured":"Nikhil Churamani, Jiaee Cheong, Sinan Kalkan, and Hatice Gunes. 2023. Towards Causal Replay for Knowledge Rehearsal in Continual Learning. In Proceedings of The First AAAI Bridge Program on Continual Causality (Proceedings of Machine Learning Research, Vol. 208), Martin Mundt, Keiland W. Cooper, Devendra Singh Dhami, Ad\u00e9le Ribeiro, James Seale Smith, Alexis Bellot, and Tyler Hayes (Eds.). PMLR, 63--70."},{"key":"e_1_3_2_2_14_1","volume-title":"Continual learning with gated incremental memories for sequential data processing","author":"Cossu Andrea","unstructured":"Andrea Cossu, Antonio Carta, and Davide Bacciu. 2020. Continual learning with gated incremental memories for sequential data processing. In IJCNN. IEEE, 1--8."},{"key":"e_1_3_2_2_15_1","unstructured":"Marco Cuturi and Mathieu Blondel. 2017. Soft-dtw: a differentiable loss function for time-series. In ICML. PMLR 894--903."},{"key":"e_1_3_2_2_16_1","first-page":"3366","article-title":"A continual learning survey: Defying forgetting in classification tasks","volume":"44","author":"Lange Matthias De","year":"2021","unstructured":"Matthias De Lange, Rahaf Aljundi, Marc Masana, Sarah Parisot, Xu Jia, Alevs Leonardis, Gregory Slabaugh, and Tinne Tuytelaars. 2021. A continual learning survey: Defying forgetting in classification tasks. TPAMI, Vol. 44, 7 (2021), 3366--3385.","journal-title":"TPAMI"},{"key":"e_1_3_2_2_17_1","volume-title":"Timevae: A variational auto-encoder for multivariate time series generation. arXiv preprint arXiv:2111.08095","author":"Desai Abhyuday","year":"2021","unstructured":"Abhyuday Desai, Cynthia Freeman, Zuhui Wang, and Ian Beaver. 2021. Timevae: A variational auto-encoder for multivariate time series generation. arXiv preprint arXiv:2111.08095 (2021)."},{"key":"e_1_3_2_2_18_1","unstructured":"Chris Donahue Julian McAuley and Miller Puckette. 2019. Adversarial Audio Synthesis. In ICLR."},{"key":"e_1_3_2_2_19_1","first-page":"14387","article-title":"Organizing recurrent network dynamics by task-computation to enable continual learning","volume":"33","author":"Duncker Lea","year":"2020","unstructured":"Lea Duncker, Laura Driscoll, Krishna V Shenoy, Maneesh Sahani, and David Sussillo. 2020. Organizing recurrent network dynamics by task-computation to enable continual learning. NIPS, Vol. 33 (2020), 14387--14397.","journal-title":"NIPS"},{"key":"e_1_3_2_2_20_1","doi-asserted-by":"publisher","DOI":"10.3390\/s19020326"},{"key":"e_1_3_2_2_21_1","volume-title":"Johannes Von Oswald, and Benjamin F Grewe","author":"Ehret Benjamin","year":"2021","unstructured":"Benjamin Ehret, Christian Henning, Maria Cervera, Alexander Meulemans, Johannes Von Oswald, and Benjamin F Grewe. 2021. Continual learning in recurrent neural networks. In ICLR."},{"key":"e_1_3_2_2_22_1","volume-title":"Dylan Freedman, Aren Jansen, Wade Lawrence, R Channing Moore, Manoj Plakal, and Marvin Ritter.","author":"Gemmeke Jort F","year":"2017","unstructured":"Jort F Gemmeke, Daniel PW Ellis, Dylan Freedman, Aren Jansen, Wade Lawrence, R Channing Moore, Manoj Plakal, and Marvin Ritter. 2017. Audio set: An ontology and human-labeled dataset for audio events. In ICASSP. IEEE, 776--780."},{"key":"e_1_3_2_2_23_1","volume-title":"Studies of mind and brain: Neural principles of learning, perception, development, cognition, and motor control","author":"Grossberg Stephen T","unstructured":"Stephen T Grossberg. 2012. Studies of mind and brain: Neural principles of learning, perception, development, cognition, and motor control. Vol. 70. Springer Science & Business Media."},{"key":"e_1_3_2_2_24_1","volume-title":"Memory augmented neural networks with wormhole connections. arXiv preprint arXiv:1701.08718","author":"Gulcehre Caglar","year":"2017","unstructured":"Caglar Gulcehre, Sarath Chandar, and Yoshua Bengio. 2017. Memory augmented neural networks with wormhole connections. arXiv preprint arXiv:1701.08718 (2017)."},{"key":"e_1_3_2_2_25_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM51629.2021.00026"},{"key":"e_1_3_2_2_26_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP.2017.7952132"},{"key":"e_1_3_2_2_27_1","volume-title":"Distilling the knowledge in a neural network. arXiv preprint arXiv:1503.02531","author":"Hinton Geoffrey","year":"2015","unstructured":"Geoffrey Hinton, Oriol Vinyals, and Jeff Dean. 2015. Distilling the knowledge in a neural network. arXiv preprint arXiv:1503.02531 (2015)."},{"key":"e_1_3_2_2_28_1","volume-title":"Zilei Wang, and Dahua Lin.","author":"Hou Saihui","year":"2019","unstructured":"Saihui Hou, Xinyu Pan, Chen Change Loy, Zilei Wang, and Dahua Lin. 2019. Learning a unified classifier incrementally via rebalancing. In CVPR. 831--839."},{"key":"e_1_3_2_2_29_1","volume-title":"Deep learning for time series classification: a review. Data mining and knowledge discovery","author":"Fawaz Hassan Ismail","year":"2019","unstructured":"Hassan Ismail Fawaz, Germain Forestier, Jonathan Weber, Lhassane Idoumghar, and Pierre-Alain Muller. 2019. Deep learning for time series classification: a review. Data mining and knowledge discovery, Vol. 33, 4 (2019), 917--963."},{"key":"e_1_3_2_2_30_1","doi-asserted-by":"publisher","DOI":"10.26555\/ijain.v2i1.54"},{"key":"e_1_3_2_2_31_1","volume-title":"Continual learning in human activity recognition: An empirical analysis of regularization. arXiv preprint arXiv:2007.03032","author":"Jha Saurav","year":"2020","unstructured":"Saurav Jha, Martin Schiemer, and Juan Ye. 2020. Continual learning in human activity recognition: An empirical analysis of regularization. arXiv preprint arXiv:2007.03032 (2020)."},{"key":"e_1_3_2_2_32_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2021.04.062"},{"key":"e_1_3_2_2_33_1","volume-title":"Continual learning of natural language processing tasks: A survey. arXiv preprint arXiv:2211.12701","author":"Ke Zixuan","year":"2022","unstructured":"Zixuan Ke and Bing Liu. 2022. Continual learning of natural language processing tasks: A survey. arXiv preprint arXiv:2211.12701 (2022)."},{"key":"e_1_3_2_2_34_1","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.1611835114"},{"key":"e_1_3_2_2_35_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-021-24483-0"},{"key":"e_1_3_2_2_36_1","volume-title":"2021 IEEE\/ACM Symposium on Edge Computing (SEC). IEEE, 319--332","author":"Kwon Young D","year":"2021","unstructured":"Young D Kwon, Jagmohan Chauhan, Abhishek Kumar, Pan Hui HKUST, and Cecilia Mascolo. 2021. Exploring system performance of continual learning for mobile and embedded sensing applications. In 2021 IEEE\/ACM Symposium on Edge Computing (SEC). IEEE, 319--332."},{"key":"e_1_3_2_2_37_1","volume-title":"Fasticarl: Fast incremental classifier and representation learning with efficient budget allocation in audio sensing applications. arXiv preprint arXiv:2106.07268","author":"Kwon Young D","year":"2021","unstructured":"Young D Kwon, Jagmohan Chauhan, and Cecilia Mascolo. 2021. Fasticarl: Fast incremental classifier and representation learning with efficient budget allocation in audio sensing applications. arXiv preprint arXiv:2106.07268 (2021)."},{"key":"e_1_3_2_2_38_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.inffus.2023.102021"},{"key":"e_1_3_2_2_39_1","volume-title":"Towards Continual Learning Desiderata via HSIC-Bottleneck Orthogonalization and Equiangular Embedding. arXiv preprint arXiv:2401.09067","author":"Li Depeng","year":"2024","unstructured":"Depeng Li, Tianqi Wang, Junwei Chen, Qining Ren, Kenji Kawaguchi, and Zhigang Zeng. 2024. Towards Continual Learning Desiderata via HSIC-Bottleneck Orthogonalization and Equiangular Embedding. arXiv preprint arXiv:2401.09067 (2024)."},{"key":"e_1_3_2_2_40_1","volume-title":"CRNet: A Fast Continual Learning Framework With Random Theory. TPAMI","author":"Li Depeng","year":"2023","unstructured":"Depeng Li and Zhigang Zeng. 2023. CRNet: A Fast Continual Learning Framework With Random Theory. TPAMI (2023)."},{"key":"e_1_3_2_2_41_1","volume-title":"Few-shot Class-incremental Audio Classification Using Stochastic Classifier. arXiv preprint arXiv:2306.02053","author":"Li Yanxiong","year":"2023","unstructured":"Yanxiong Li, Wenchang Cao, Jialong Li, Wei Xie, and Qianhua He. 2023. Few-shot Class-incremental Audio Classification Using Stochastic Classifier. arXiv preprint arXiv:2306.02053 (2023)."},{"key":"e_1_3_2_2_42_1","volume-title":"Few-shot Class-incremental Audio Classification Using Dynamically Expanded Classifier with Self-attention Modified Prototypes","author":"Li Yanxiong","year":"2023","unstructured":"Yanxiong Li, Wenchang Cao, Wei Xie, Jialong Li, and Emmanouil Benetos. 2023. Few-shot Class-incremental Audio Classification Using Dynamically Expanded Classifier with Self-attention Modified Prototypes. IEEE Transactions on Multimedia (2023)."},{"key":"e_1_3_2_2_43_1","volume-title":"Learning Without Forgetting","author":"Li Zhizhong","unstructured":"Zhizhong Li and Derek Hoiem. 2016. Learning Without Forgetting. In ECCV. Springer, 614--629."},{"key":"e_1_3_2_2_44_1","doi-asserted-by":"publisher","DOI":"10.1109\/PERCOM.2009.4912759"},{"key":"e_1_3_2_2_45_1","doi-asserted-by":"crossref","unstructured":"Yaoyao Liu Yuting Su An-An Liu Bernt Schiele and Qianru Sun. 2020. Mnemonics training: Multi-class incremental learning without forgetting. In CVPR. 12245--12254.","DOI":"10.1109\/CVPR42600.2020.01226"},{"key":"e_1_3_2_2_46_1","unstructured":"David Lopez-Paz and Marc'Aurelio Ranzato. 2017. Gradient episodic memory for continual learning. In NeurIPS. 6467--6476."},{"key":"e_1_3_2_2_47_1","volume-title":"Multi-objective Learning to Overcome Catastrophic Forgetting in Time-series Applications. ACM Transactions on Knowledge Discovery from Data (TKDD)","author":"Mahmoud Reem A","year":"2022","unstructured":"Reem A Mahmoud and Hazem Hajj. 2022. Multi-objective Learning to Overcome Catastrophic Forgetting in Time-series Applications. ACM Transactions on Knowledge Discovery from Data (TKDD), Vol. 16, 6 (2022), 1--20."},{"key":"e_1_3_2_2_48_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2021.10.021"},{"key":"e_1_3_2_2_49_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2022.3213473"},{"key":"e_1_3_2_2_50_1","volume-title":"Psychology of learning and motivation.","author":"McCloskey Michael","unstructured":"Michael McCloskey and Neal J Cohen. 1989. Catastrophic interference in connectionist networks: The sequential learning problem. In Psychology of learning and motivation. Vol. 24. Elsevier, 109--165."},{"key":"e_1_3_2_2_51_1","volume-title":"A time series is worth 64 words: Long-term forecasting with transformers. arXiv preprint arXiv:2211.14730","author":"Nie Yuqi","year":"2022","unstructured":"Yuqi Nie, Nam H Nguyen, Phanwadee Sinthong, and Jayant Kalagnanam. 2022. A time series is worth 64 words: Long-term forecasting with transformers. arXiv preprint arXiv:2211.14730 (2022)."},{"key":"e_1_3_2_2_52_1","volume-title":"Continual Normalization: Rethinking Batch Normalization for Online Continual Learning. In ICLR.","author":"Pham Quang","year":"2022","unstructured":"Quang Pham, Chenghao Liu, and HOI Steven. 2022. Continual Normalization: Rethinking Batch Normalization for Online Continual Learning. In ICLR."},{"key":"e_1_3_2_2_53_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41597-022-01836-y"},{"key":"e_1_3_2_2_54_1","volume-title":"Ponnuthurai Nagaratnam Suganthan, and Ramasamy Savitha","author":"Qiao Zhongzheng","year":"2023","unstructured":"Zhongzheng Qiao, Minghui Hu, Xudong Jiang, Ponnuthurai Nagaratnam Suganthan, and Ramasamy Savitha. 2023. Class-Incremental Learning on Multivariate Time Series Via Shape-Aligned Temporal Distillation. In ICASSP. IEEE, 1--5."},{"key":"e_1_3_2_2_55_1","volume-title":"TFB: Towards Comprehensive and Fair Benchmarking of Time Series Forecasting Methods. arXiv preprint arXiv:2403.20150","author":"Qiu Xiangfei","year":"2024","unstructured":"Xiangfei Qiu, Jilin Hu, Lekui Zhou, Xingjian Wu, Junyang Du, Buang Zhang, Chenjuan Guo, Aoying Zhou, Christian S Jensen, Zhenli Sheng, et al. 2024. TFB: Towards Comprehensive and Fair Benchmarking of Time Series Forecasting Methods. arXiv preprint arXiv:2403.20150 (2024)."},{"key":"e_1_3_2_2_56_1","volume-title":"Chuan-Sheng Foo, Zhenghua Chen, Min Wu, Chee-Keong Kwoh, and Xiaoli Li.","author":"Ragab Mohamed","year":"2023","unstructured":"Mohamed Ragab, Emadeldeen Eldele, Wee Ling Tan, Chuan-Sheng Foo, Zhenghua Chen, Min Wu, Chee-Keong Kwoh, and Xiaoli Li. 2023. Adatime: A benchmarking suite for domain adaptation on time series data. ACM Transactions on Knowledge Discovery from Data, Vol. 17, 8 (2023), 1--18."},{"key":"e_1_3_2_2_57_1","unstructured":"Sylvestre-Alvise Rebuffi Alexander Kolesnikov Georg Sperl and Christoph H Lampert. 2017. icarl: Incremental classifier and representation learning. In CVPR. 2001--2010."},{"key":"e_1_3_2_2_58_1","unstructured":"Matthew Riemer Ignacio Cases Robert Ajemian Miao Liu Irina Rish Yuhai Tu and Gerald Tesauro. 2018. Learning to Learn without Forgetting by Maximizing Transfer and Minimizing Interference. In ICLR."},{"key":"e_1_3_2_2_59_1","unstructured":"David Rolnick Arun Ahuja Jonathan Schwarz Timothy P Lillicrap and Greg Wayne. 2019. Experience replay for continual learning. In NeurIPS. 350--360."},{"key":"e_1_3_2_2_60_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-015-0816-y"},{"key":"e_1_3_2_2_61_1","volume-title":"Online continual learning for human activity recognition. Pervasive and Mobile Computing","author":"Schiemer Martin","year":"2023","unstructured":"Martin Schiemer, Lei Fang, Simon Dobson, and Juan Ye. 2023. Online continual learning for human activity recognition. Pervasive and Mobile Computing (2023), 101817."},{"key":"e_1_3_2_2_62_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.smhl.2021.100232"},{"key":"e_1_3_2_2_63_1","doi-asserted-by":"crossref","unstructured":"Dongsub Shim Zheda Mai Jihwan Jeong Scott Sanner Hyunwoo Kim and Jongseong Jang. 2021. Online Class-Incremental Continual Learning with Adversarial Shapley Value. In AAAI. 9630--9638.","DOI":"10.1609\/aaai.v35i11.17159"},{"key":"e_1_3_2_2_64_1","volume-title":"Jaehong Kim, and Jiwon Kim.","author":"Shin Hanul","year":"2017","unstructured":"Hanul Shin, Jung Kwon Lee, Jaehong Kim, and Jiwon Kim. 2017. Continual learning with deep generative replay. In NeurIPS. 2990--2999."},{"key":"e_1_3_2_2_65_1","unstructured":"James Seale Smith Junjiao Tian Shaunak Halbe Yen-Chang Hsu and Zsolt Kira. 2023. A closer look at rehearsal-free continual learning. In CVPR. 2409--2419."},{"key":"e_1_3_2_2_66_1","volume-title":"Toward training recurrent neural networks for lifelong learning. Neural computation","author":"Sodhani Shagun","year":"2020","unstructured":"Shagun Sodhani, Sarath Chandar, and Yoshua Bengio. 2020. Toward training recurrent neural networks for lifelong learning. Neural computation, Vol. 32, 1 (2020), 1--35."},{"key":"e_1_3_2_2_67_1","volume-title":"Few-Shot Class-Incremental Learning for Medical Time Series Classification","author":"Sun Le","year":"2023","unstructured":"Le Sun, Mingyang Zhang, Benyou Wang, and Prayag Tiwari. 2023. Few-Shot Class-Incremental Learning for Medical Time Series Classification. IEEE Journal of Biomedical and Health Informatics (2023)."},{"key":"e_1_3_2_2_68_1","volume-title":"Instance normalization: The missing ingredient for fast stylization. arXiv preprint arXiv:1607.08022","author":"Ulyanov Dmitry","year":"2016","unstructured":"Dmitry Ulyanov, Andrea Vedaldi, and Victor Lempitsky. 2016. Instance normalization: The missing ingredient for fast stylization. arXiv preprint arXiv:1607.08022 (2016)."},{"key":"e_1_3_2_2_69_1","volume-title":"Three scenarios for continual learning. arXiv preprint arXiv:1904.07734","author":"van de Ven Gido M","year":"2019","unstructured":"Gido M van de Ven and Andreas S Tolias. 2019. Three scenarios for continual learning. arXiv preprint arXiv:1904.07734 (2019)."},{"key":"e_1_3_2_2_70_1","volume-title":"Three types of incremental learning. Nature Machine Intelligence","author":"van de Ven Gido M","year":"2022","unstructured":"Gido M van de Ven, Tinne Tuytelaars, and Andreas S Tolias. 2022. Three types of incremental learning. Nature Machine Intelligence (2022), 1--13."},{"key":"e_1_3_2_2_71_1","doi-asserted-by":"publisher","DOI":"10.1145\/3147.3165"},{"key":"e_1_3_2_2_72_1","volume-title":"A comprehensive survey of continual learning: Theory, method and application. arXiv preprint arXiv:2302.00487","author":"Wang Liyuan","year":"2023","unstructured":"Liyuan Wang, Xingxing Zhang, Hang Su, and Jun Zhu. 2023. A comprehensive survey of continual learning: Theory, method and application. arXiv preprint arXiv:2302.00487 (2023)."},{"key":"e_1_3_2_2_73_1","doi-asserted-by":"publisher","DOI":"10.1109\/TCSVT.2023.3262739"},{"key":"e_1_3_2_2_74_1","doi-asserted-by":"publisher","DOI":"10.1145\/3581783.3611926"},{"key":"e_1_3_2_2_75_1","doi-asserted-by":"publisher","DOI":"10.1109\/WASPAA.2019.8937236"},{"key":"e_1_3_2_2_76_1","doi-asserted-by":"publisher","DOI":"10.1109\/WASPAA.2019.8937236"},{"key":"e_1_3_2_2_77_1","doi-asserted-by":"crossref","unstructured":"Zifeng Wang Zizhao Zhang Chen-Yu Lee Han Zhang Ruoxi Sun Xiaoqi Ren Guolong Su Vincent Perot Jennifer Dy and Tomas Pfister. 2022. Learning to prompt for continual learning. In CVPR. 139--149.","DOI":"10.1109\/CVPR52688.2022.00024"},{"key":"e_1_3_2_2_78_1","first-page":"133190","article-title":"Wisdm smartphone and smartwatch activity and biometrics dataset","volume":"7","author":"Weiss Gary M","year":"2019","unstructured":"Gary M Weiss. 2019. Wisdm smartphone and smartwatch activity and biometrics dataset. UCI Machine Learning Repository: WISDM Smartphone and Smartwatch Activity and Biometrics Dataset Data Set, Vol. 7 (2019), 133190--133202.","journal-title":"UCI Machine Learning Repository: WISDM Smartphone and Smartwatch Activity and Biometrics Dataset Data Set"},{"key":"e_1_3_2_2_79_1","volume-title":"Timesnet: Temporal 2d-variation modeling for general time series analysis. In The eleventh international conference on learning representations.","author":"Wu Haixu","year":"2022","unstructured":"Haixu Wu, Tengge Hu, Yong Liu, Hang Zhou, Jianmin Wang, and Mingsheng Long. 2022. Timesnet: Temporal 2d-variation modeling for general time series analysis. In The eleventh international conference on learning representations."},{"key":"e_1_3_2_2_80_1","volume-title":"International Conference on Learning Representations.","author":"Yao Weiran","year":"2022","unstructured":"Weiran Yao, Yuewen Sun, Alex Ho, Changyin Sun, and Kun Zhang. 2022. Learning Temporally Causal Latent Processes from General Temporal Data. In International Conference on Learning Representations."},{"key":"e_1_3_2_2_81_1","unstructured":"Hongxu Yin Pavlo Molchanov Jose M Alvarez Zhizhong Li Arun Mallya Derek Hoiem Niraj K Jha and Jan Kautz. 2020. Dreaming to distill: Data-free knowledge transfer via deepinversion. In CVPR. 8715--8724."},{"key":"e_1_3_2_2_82_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neunet.2022.10.031"},{"key":"e_1_3_2_2_83_1","volume-title":"Continual Learning by Modeling Intra-Class Variation. Transactions on Machine Learning Research","author":"Yu Longhui","year":"2023","unstructured":"Longhui Yu, Tianyang Hu, Lanqing HONG, Zhen Liu, Adrian Weller, and Weiyang Liu. 2023. Continual Learning by Modeling Intra-Class Variation. Transactions on Machine Learning Research (2023)."},{"key":"e_1_3_2_2_84_1","doi-asserted-by":"crossref","unstructured":"Yunfei Zhang Xiaoyang Huo Tianyi Chen Si Wu and Hau San Wong. 2023. Exploring Intra-class Variation Factors with Learnable Cluster Prompts for Semi-supervised Image Synthesis. In CVPR. 7392--7401.","DOI":"10.1109\/CVPR52729.2023.00714"},{"key":"e_1_3_2_2_85_1","volume-title":"Deep class-incremental learning: A survey. arXiv preprint arXiv:2302.03648","author":"Zhou Da-Wei","year":"2023","unstructured":"Da-Wei Zhou, Qi-Wei Wang, Zhi-Hong Qi, Han-Jia Ye, De-Chuan Zhan, and Ziwei Liu. 2023. Deep class-incremental learning: A survey. arXiv preprint arXiv:2302.03648 (2023)."},{"key":"e_1_3_2_2_86_1","unstructured":"Tian Zhou Peisong Niu Liang Sun Rong Jin et al. 2023. One fits all: Power general time series analysis by pretrained lm. Advances in neural information processing systems Vol. 36 (2023) 43322--43355."},{"key":"e_1_3_2_2_87_1","doi-asserted-by":"crossref","unstructured":"Fei Zhu Xu-Yao Zhang Chuang Wang Fei Yin and Cheng-Lin Liu. 2021. Prototype Augmentation and Self-Supervision for Incremental Learning. In CVPR. 5871--5880.","DOI":"10.1109\/CVPR46437.2021.00581"}],"event":{"name":"KDD '24: The 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","location":"Barcelona Spain","acronym":"KDD '24","sponsor":["SIGMOD ACM Special Interest Group on Management of Data","SIGKDD ACM Special Interest Group on Knowledge Discovery in Data"]},"container-title":["Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3637528.3671581","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3637528.3671581","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T00:04:19Z","timestamp":1750291459000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3637528.3671581"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,8,24]]},"references-count":87,"alternative-id":["10.1145\/3637528.3671581","10.1145\/3637528"],"URL":"https:\/\/doi.org\/10.1145\/3637528.3671581","relation":{},"subject":[],"published":{"date-parts":[[2024,8,24]]},"assertion":[{"value":"2024-08-24","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}