[43] Wouter F. Schmidt, Martin A. Kraaijveld, and Robert P. W. Duin. Feedforward neural networks with random weights. In Proc. 11th IAPR International Conference on Pattern Recognition. Vol.II. Conference B: Pattern Recognition Methodology and Systems, pages 1-4, 1992. 2, 3, 6, 7
[44] Murray Shanahan, Christos Kaplanis, and Jovana Mitrovic. Encoders and ensembles for task-free continual learning. arXiv:2105.13327, 2021. 27
[45] Aliaksandra Shysheya, John Bronskill, Massimiliano Patacchiola, Sebastian Nowozin, and Richard E Turner. FiT: Parameter efficient few-shot transfer learning for personalized and federated image classification. arXiv:2206.08671, 2023. 10
[46] James Seale Smith, Leonid Karlinsky, Vyshnavi Gutta, Paola Cascante-Bonilla, Donghyun Kim, Assaf Arbelle, Rameswar Panda, Rogerio Feris, and Zsolt Kira. CODA-Prompt: COntinual Decomposed Attention-based prompting for rehearsal-free continual learning. In Proc. IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 11909-11919, June 2023. 2, 3, 8, 9, 10, 16
[47] James Seale Smith, Junjiao Tian, Shaunak Halbe, Yen-Chang Hsu, and Zsolt Kira. A closer look at rehearsal-free continual learning. arXiv:2203.17269 (Revised 3 April, 2023. 2, 3, 9, 10, 16
[48] Albin Soutif-Cormerais, Marc Masana, Joost van de Weijer, and Bartlomiej Twardowski. On the importance of cross-task features for class-incremental learning. arXiv:2106.11930, 2021. 1
[49] Hai-Long Sun, Da-Wei Zhou, Han-Jia Ye, and De-Chuan Zhan. PILOT: A pre-trained model-based continual learning toolbox. arXiv:2309.07117, 2023. 9
[50] Michael Tsang, Dehua Cheng, and Yan Liu. Detecting statistical interactions from neural network weights. In Proc. Intl Conference on Learning Representations (ICLR), 2018. 6
[51] Gido M. van de Ven, Tinne Tuytelaars, and Andreas S. Tolias. Three types of incremental learning. Nature Machine Intelligence, pages 1185–1197, 2022. 1, 2, 4
[52] Catherine Wah, Steve Branson, Peter Welinder, Pietro Perona, and Serge Belongie. The Caltech-UCSD Birds-200-2011 Dataset. Technical report, California Institute of Technology, 2011. Technical Report CNS-TR-2011-001. 21
[53] Liuyuan Wang, Xingxing Zhang, Hang Su, and Jun Zhu. A comprehensive survey of continual learning: Theory, method and application. arXiv: 2302.00487, 2023. 1, 2, 3
[54] Ruoxi Wang, Bin Fu, Gang Fu, and Mingliang Wang. Deep & cross network for ad click predictions. arXiv:1708.05123 (ADKDD'17), 2017. 6
[55] Yabin Wang, Zhiwu Huang, and Xiaopeng Hong. S-Prompts learning with pre-trained transformers: An Occam's razor for domain incremental learning. In S. Koyejo, S. Mohamed, A. Agarwal, D. Belgrave, K. Cho, and A. Oh, editors, Advances in Neural Information Processing Systems, volume 35, pages 5682-5695. Curran Associates, Inc., 2022. 2, 3, 10, 16
[56] Zifeng Wang, Zizhao Zhang, Sayna Ebrahimi, Ruoxi Sun, Han Zhang, Chen-Yu Lee, Xiaoqi Ren, Guolong Su, Vincent Perot, Jennifer Dy, and Tomas Pfister. DualPrompt: Complementary prompting for rehearsal-free continual learning. In Computer Vision - ECCV 2022: 17th European Conference, Tel Aviv, Israel, October 23-27, 2022, Proceedings, Part XXVI, page 631-648, Berlin, Heidelberg, 2022. Springer-Verlag. ISBN 978-3-031-19808-3. 2, 3, 5, 8, 9, 16, 21
[57] Zifeng Wang, Zizhao Zhang, Chen-Yu Lee, Han Zhang, Ruoxi Sun, Xiaoqi Ren, Guolong Su, Vincent Perot, Jennifer G. Dy, and Tomas Pfister. Learning to prompt for continual learning. In Proc. IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2022, New Orleans, LA, USA, June 18-24, 2022, pages 139-149. IEEE, 2022. 2, 3, 8, 9, 16, 27
[58] Andrew R. Webb and Keith D. Copsey. Statistical Pattern Recognition. John Wiley & Sons, Ltd, 2011. 4