This repository collects a list (non-exhaustive and under-development) of recent continual learning (CL) papers, with the intention of providing the reader with a structured overview of the state-of-the-art CL papers divided by the category (e.g., rehearsal, regularization, architectural) and the modality (e.g., audio/speech, text/NLP, image/vision) considered in the papers. Given my constant quest to keep up with brand-new papers, I hope this collection can come in handy for other people.

I would like to include papers that in my opinion are compelling and noteworthy, nevertheless any suggestion is warmly appreciated, so if you think a paper is worth being listed here, please let me know!

Legend:

• ARC: architectural-based approach; REH: rehearsal-based approach; REG: regularization-based approach.

• G. I. Parisi, R. Kemker, J. L. Part, C. Kanan, and S. Wermter, “Continual lifelong learning with neural networks: A review,” Neural Networks, vol. 113, pp. 54–71, 2019. [Paper]
• T. Lesort, V. Lomonaco, A. Stoian, D. Maltoni, D. Filliat, and N. Diaz-Rodriguez, "Continual learning for robotics: Definition, framework, learning strategies, opportunities and challenges". Information Fusion, 58:52–68, 2020. [Paper]
• M. De Lange, R. Aljundi, M.Masana, et al., “A continual learning survey: Defying forgetting in classification tasks,” TPAMI, vol. 44, no. 7, pp. 3366–3385, 2021. [Paper]
• L. Wang, X. Zhang, H. Su, and J. Zhu. "A comprehensive survey of continual learning: Theory, method and application". arXiv preprint, 2023. [Paper]
• D. Zhou, Q. Wang, Z. Qi, H. Ye, D. Zhan, and Z. Liu, "Deep Class-Incremental Learning: A Survey ", arXiv preprint, 2023. [Paper]
• Wang, Z., Yang, E., Shen, L. and Huang, H., "A Comprehensive Survey of Forgetting in Deep Learning Beyond Continual Learning", arXiv preprint, 2023. [Paper]
• Kilickaya, M., van de Weijer, J. and Asano, "Towards Label-Efficient Incremental Learning: A Survey", arXiv preprint, 2023. [Paper]

• Wang, K., Herranz, L. and van de Weijer, J., "Continual learning in cross-modal retrieval", CVPR, 2021. REG [Paper]
• Wang, Z., Zhang, Z., Lee, C.Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J. and Pfister, T., "Learning to prompt for continual learning", CVPR, 2022. ARC [Paper]
• Sun, S., Calandriello, D., Hu, H., Li, A. and Titsias, M., "Information-theoretic online memory selection for continual learning", ICLR, 2022. REH [Paper]
• Boschini, M., Bonicelli, L., Buzzega, P., Porrello, A. and Calderara, S., "Class-incremental continual learning into the extended der-verse", TPAMI, 2022. REH + REG [Paper]
• Smith, J. and Karlinsky, L. and Gutta, V. and Cascante-Bonilla, P. et al., "CODA-Prompt: COntinual Decomposed Attention-based Prompting for Rehearsal-Free Continual Learning", CVPR, 2023. ARC [Paper]
• Jeeveswaran K, Bhat P, Zonooz B, Arani E., "BiRT: Bio-inspired Replay in Vision Transformers for Continual Learning", ICML, 2023. REH + REG [Paper]
• Frascaroli, E., Benaglia, R., Boschini, M., Moschella, L., Fiorini, C., Rodolà, E. and Calderara, S., "CaSpeR: Latent Spectral Regularization for Continual Learning", arxiv preprint, 2023. REG [Paper]
• Jiang, S., Fang, Y., Zhang, H., Wang, P., Qi, Y. and Liu, Q., "Teacher Agent: A Non-Knowledge Distillation Method for Rehearsal-based Video Incremental Learning", arxiv preprint, 2023. REH + REG [Paper]
• S. Paul, L. Frey, R. Kamath, K. Kersting, M. Mundt, "Masked Autoencoders are Efficient Continual Federated Learners", arxiv preprint, 2023. REG [Paper]
• De Lange, M., van de Ven, G. and Tuytelaars, T., "Continual evaluation for lifelong learning: Identifying the stability gap", ICLR, 2023. [Paper]
• Kang, Z., Fini, E., Nabi, M., Ricci, E. and Alahari, K., "A soft nearest-neighbor framework for continual semi-supervised learning", ICCV, 2023. REG + REH [Paper]
• Yoon, J., Hwang, S.J. and Cao, Y., "Continual Learners are Incremental Model Generalizers", ICML, 2023. REG + ARC [Paper]
• Zhao, H., Zhou, T., Long, G., Jiang, J. and Zhang, C., "Does Continual Learning Equally Forget All Parameters?", ICML, 2023. REH [Paper]
• Wang, Z., Zhan, Z., Gong, Y., Shao, Y., Ioannidis, S., Wang, Y. and Dy, J., "DualHSIC: HSIC-Bottleneck and Alignment for Continual Learning", ICML, 2023. REH + REG [Paper]
• Ni, Z., Wei, L., Tang, S., Zhuang, Y. and Tian, Q., "Continual Vision-Language Representaion Learning with Off-Diagonal Information", ICML, 2023. REG [Paper]
• Hu, Z., Lyu, J., Gao, D. and Vasconcelos, N., POP: Prompt Of Prompts for Continual Learning, arxiv preprint, 2023. REH + ARCH [Paper]
• Zhai, J.T., Liu, X., Bagdanov, A.D., Li, K. and Cheng, M.M., Masked Autoencoders are Efficient Class Incremental Learners, ICCV, 2023. REH + REG [Paper]
• Khan, M.G.Z.A., Naeem, M.F., Van Gool, L., Stricker, D., Tombari, F. and Afzal, M.Z., Introducing Language Guidance in Prompt-based Continual Learning, ICCV, 2023. ARCH [Paper]

• Yang, G., Fini, E., Xu, D., Rota, P., Ding, M., Nabi, M., Alameda-Pineda, X. and Ricci, E., "Uncertainty-aware contrastive distillation for incremental semantic segmentation", TPAMI, 2022. REG [Paper]
• Zhao, D., Yuan, B. and Shi, Z., "Inherit With Distillation and Evolve With Contrast: Exploring Class Incremental Semantic Segmentation Without Exemplar Memory", TPAMI, 2023. REG [Paper]
• Cermelli, F., Cord, M. and Douillard, A., "CoMFormer: Continual Learning in Semantic and Panoptic Segmentation", CVPR, 2023. REG [Paper]

• U. Cappellazzo, D. Falavigna, and A. Brutti, "An Investigation of the Combination of Rehearsal and Knowledge Distillation in Continual Learning for Spoken Language Understanding", INTERSPEECH, 2023. REH + REG [Paper]
• Wang, Z., Subakan, C., Jiang, X., Wu, J., Tzinis, E., Ravanelli, M. and Smaragdis, P., "Learning Representations for New Sound Classes With Continual Self-Supervised Learning", IEEE Signal Processing Letters, 2023. REG [Paper]
• U. Cappellazzo, M. Yang, D. Falavigna, and A. Brutti, "Sequence-Level Knowledge Distillation for Class-Incremental End-to-End Spoken Language Understanding", INTERSPEECH, 2023. REH + REG [Paper]
• Jiang, X., Li, Y.A. and Mesgarani, N., "DeCoR: Defy Knowledge Forgetting by Predicting Earlier Audio Codes", INTERSPEECH, 2023. REG [Paper]
• Diwan, A., Yeh, C.F., Hsu, W.N., Tomasello, P., Choi, E., Harwath, D. and Mohamed, A., "Continual Learning for On-Device Speech Recognition using Disentangled Conformers", ICASSP, 2023. ARC [Paper]
• Selvaraj, N.M., Guo, X., Kong, A., Shen, B. and Kot, A., Adapter Incremental Continual Learning of Efficient Audio Spectrogram Transformers, INTERSPEECH, 2023. ARC [Paper]
• Michieli, U., Parada, P.P. and Ozay, M., "Online Continual Learning in Keyword Spotting for Low-Resource Devices via Pooling High-Order Temporal Statistics", INTERSPEECH, 2023. REG [Paper]

• Cao, Y., Wei, H.R., Chen, B. and Wan, X., "Continual learning for neural machine translation", NAACL-HLT, 2021. REG [Paper]
• A. Razdaibiedina, Y. Mao and R. Hou, M. Khabsa, M. Lewis, and A. Almahairi, "Progressive prompts: Continual learning for language models", ICLR, 2023. ARC [Paper]
• Ke, Z., Shao, Y., Lin, H., Konishi, T., Kim, G. and Liu, B., "Continual Pre-training of Language Models", ICLR, 2023. REG [Paper]
• Wang, J., Dong, D., Shou, L., Chen, K. and Chen, G., "Effective Continual Learning for Text Classification with Lightweight Snapshots", AAAI, 2023. REG + ARC [Paper]

• D. Zhou, Y. Zhang, J. Ning, H. Ye, D. Zhan, and Z. Liu, "Learning without Forgetting for Vision-Language Models", arXiv preprint, 2023. ARC [Paper]
• He X, Feng W, Fu TJ, Jampani V, Akula A, Narayana P, Basu S, Wang WY, Wang XE, "Discriminative Diffusion Models as Few-shot Vision and Language Learners", arxiv preprint, 2023. ARC [Paper]