[29] Bo Li, Yifei Shen, Jingkang Yang, Yezhen Wang, Jiawei Ren, Tong Che, Jun Zhang, and Ziwei Liu. Sparse mixture-of-experts are domain generalizable learners. In The Eleventh International Conference on Learning Representations, 2023.
[30] Xilai Li, Yingbo Zhou, Tianfu Wu, Richard Socher, and Caiming Xiong. Learn to grow: A continual structure learning framework for overcoming catastrophic forgetting. In International Conference on Machine Learning, pages 3925-3934. PMLR, 2019.
[31] Zeqian Li, Michael Mozer, and Jacob Whitehill. Compositional embeddings for multi-label one-shot learning. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), pages 296-304, January 2021.
[32] Zeqian Li and Jacob Whitehill. Compositional embedding models for speaker identification and diarization with simultaneous speech from 2+ speakers. In ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pages 7163-7167. IEEE, 2021.
[33] Zhizhong Li and Derek Hoiem. Learning without forgetting. IEEE transactions on pattern analysis and machine intelligence, 40(12):2935-2947, 2017.
[34] Weixin Liang and James Zou. MetaShift: A dataset of datasets for evaluating contextual distribution shifts and training conflicts. In International Conference on Learning Representations, 2022.
[35] David Lopez-Paz and Marc'Aurelio Ranzato. Gradient episodic memory for continual learning. Advances in neural information processing systems, 30, 2017.
[36] Arun Mallya and Svetlana Lazebnik. Packnet: Adding multiple tasks to a single network by iterative pruning. In 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 7765-7773, 2018.
[37] Michael McCloskey and Neal J Cohen. Catastrophic interference in connectionist networks: The sequential learning problem. In *Psychology of learning and motivation*, volume 24, pages 109–165. Elsevier, 1989.
[38] Jorge A Mendez and Eric Eaton. How to reuse and compose knowledge for a lifetime of tasks: A survey on continual learning and functional composition. arXiv preprint arXiv:2207.07730, 2022.
[39] Richard Montague and Richmond H Thomason. Formal philosophy. selected papers of richard montague. Erkenntnis, 9(2), 1975.
[40] Tushar Nagarajan and Kristen Grauman. Attributes as operators: Factorizing unseen attribute-object compositions. In Proceedings of the European Conference on Computer Vision (ECCV), September 2018.
[41] Oleksiy Ostapenko, Pau Rodriguez, Massimo Caccia, and Laurent Charlin. Continual learning via local module composition. Advances in Neural Information Processing Systems, 34:30298-30312, 2021.
[42] Namuk Park and Songkuk Kim. How do vision transformers work? In International Conference on Learning Representations, 2022.
[43] Jathushan Rajasegaran, Munawar Hayat, Salman H Khan, Fahad Shahbaz Khan, and Ling Shao. Random path selection for continual learning. Advances in Neural Information Processing Systems, 32, 2019.
[44] Lukas Schott, Julius Von Kugelgen, Frederik Träuble, Peter Gehler, Chris Russell, Matthias Bethge, Bernhard Schölkopf, Francesco Locatello, and Wieland Brendel. Visual representation learning does not generalize strongly within the same domain. In International Conference on Learning Representations, 2022.
[45] Shuai Shao, Zeming Li, Tianyuan Zhang, Chao Peng, Gang Yu, Xiangyu Zhang, Jing Li, and Jian Sun. Objects365: A large-scale, high-quality dataset for object detection. In Proceedings of the IEEE/CVF international conference on computer vision, pages 8430-8439, 2019.