ref.bib

@misc{wang2023comprehensive,
      title={A Comprehensive Survey of Continual Learning: Theory, Method and Application}, 
      author={Liyuan Wang and Xingxing Zhang and Hang Su and Jun Zhu},
      year={2023},
      eprint={2302.00487},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}

@misc{chen2023dynamic,
      title={Dynamic Residual Classifier for Class Incremental Learning}, 
      author={Xiuwei Chen and Xiaobin Chang},
      year={2023},
      eprint={2308.13305},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}

@misc{dong2023heterogeneous,
      title={Heterogeneous Forgetting Compensation for Class-Incremental Learning}, 
      author={Jiahua Dong and Wenqi Liang and Yang Cong and Gan Sun},
      year={2023},
      eprint={2308.03374},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}

@misc{li2017learning,
      title={Learning without Forgetting}, 
      author={Zhizhong Li and Derek Hoiem},
      year={2017},
      eprint={1606.09282},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}

@article{Gou_2021,
   title={Knowledge Distillation: A Survey},
   volume={129},
   ISSN={1573-1405},
   url={http://dx.doi.org/10.1007/s11263-021-01453-z},
   DOI={10.1007/s11263-021-01453-z},
   number={6},
   journal={International Journal of Computer Vision},
   publisher={Springer Science and Business Media LLC},
   author={Gou, Jianping and Yu, Baosheng and Maybank, Stephen J. and Tao, Dacheng},
   year={2021},
   month=mar, pages={1789–1819} }

@misc{shin2017continual,
      title={Continual Learning with Deep Generative Replay}, 
      author={Hanul Shin and Jung Kwon Lee and Jaehong Kim and Jiwon Kim},
      year={2017},
      eprint={1705.08690},
      archivePrefix={arXiv},
      primaryClass={cs.AI}
}

@misc{ayub2021eec,
      title={EEC: Learning to Encode and Regenerate Images for Continual Learning}, 
      author={Ali Ayub and Alan R. Wagner},
      year={2021},
      eprint={2101.04904},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}

@misc{ostapenko2019learning,
      title={Learning to Remember: A Synaptic Plasticity Driven Framework for Continual Learning}, 
      author={Oleksiy Ostapenko and Mihai Puscas and Tassilo Klein and Patrick Jähnichen and Moin Nabi},
      year={2019},
      eprint={1904.03137},
      archivePrefix={arXiv},
      primaryClass={cs.NE}
}

@misc{kemker2018fearnet,
      title={FearNet: Brain-Inspired Model for Incremental Learning}, 
      author={Ronald Kemker and Christopher Kanan},
      year={2018},
      eprint={1711.10563},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}
@article{Riemer_Klinger_Bouneffouf_Franceschini_2019,
	title = {Scalable Recollections for Continual Lifelong Learning},
	volume = {33},
	url = {https://ojs.aaai.org/index.php/AAAI/article/view/3935},
	doi = {10.1609/aaai.v33i01.33011352},
	abstractnote = {<p>Given the recent success of Deep Learning applied to a variety of single tasks, it is natural to consider more human-realistic settings. Perhaps the most difficult of these settings is that of continual lifelong learning, where the model must learn online over a continuous stream of non-stationary data. A successful continual lifelong learning system must have three key capabilities: it must <em>learn and adapt</em> over time, it must <em>not forget</em> what it has learned, and it must be <em>efficient</em> in both training time and memory. Recent techniques have focused their efforts primarily on the first two capabilities while questions of efficiency remain largely unexplored. In this paper, we consider the problem of efficient and effective storage of experiences over very large time-frames. In particular we consider the case where typical experiences are <em>O</em>(<em>n</em>) bits and memories are limited to <em>O</em>(<em>k</em>) bits for <em>k << n</em>. We present a novel scalable architecture and training algorithm in this challenging domain and provide an extensive evaluation of its performance. Our results show that we can achieve considerable gains on top of state-of-the-art methods such as GEM. <sup>1</sup></p>},
	number = {01},
	journal = {Proceedings of the AAAI Conference on Artificial Intelligence},
	author = {Riemer, Matthew and Klinger, Tim and Bouneffouf, Djallel and Franceschini, Michele},
	year = {2019},
	month = {Jul.},
	pages = {1352-1359}
}

@misc{rostami2019complementary,
      title={Complementary Learning for Overcoming Catastrophic Forgetting Using Experience Replay}, 
      author={Mohammad Rostami and Soheil Kolouri and Praveen K. Pilly},
      year={2019},
      eprint={1903.04566},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}

@misc{lopezpaz2022gradient,
      title={Gradient Episodic Memory for Continual Learning}, 
      author={David Lopez-Paz and Marc'Aurelio Ranzato},
      year={2022},
      eprint={1706.08840},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}

@misc{chaudhry2019efficient,
      title={Efficient Lifelong Learning with A-GEM}, 
      author={Arslan Chaudhry and Marc'Aurelio Ranzato and Marcus Rohrbach and Mohamed Elhoseiny},
      year={2019},
      eprint={1812.00420},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}

@article{Zeng_2019,
	   title={Continual learning of context-dependent processing in neural networks},
	   volume={1},
	   ISSN={2522-5839},
	   url={http://dx.doi.org/10.1038/s42256-019-0080-x},
	   DOI={10.1038/s42256-019-0080-x},
	   number={8},
	   journal={Nature Machine Intelligence},
	   publisher={Springer Science and Business Media LLC},
	   author={Zeng, Guanxiong and Chen, Yang and Cui, Bo and Yu, Shan},
	   year={2019},
	   month=aug, pages={364–372} 
   }

@inproceedings{Guo2022AdaptiveOP, 
	title={Adaptive Orthogonal Projection for Batch and Online Continual Learning}, 
	author={Yiduo Guo and Wenpeng Hu and Dongyan Zhao and Bing Liu}, 
	booktitle={AAAI Conference on Artificial Intelligence}, 
	year={2022}, 
    url={https://api.semanticscholar.org/CorpusID:250290748} 
}

@inproceedings{NEURIPS2020_518a38cc,
	author = {Mirzadeh, Seyed Iman and Farajtabar, Mehrdad and Pascanu, Razvan and Ghasemzadeh, Hassan},
	booktitle = {Advances in Neural Information Processing Systems},
	editor = {H. Larochelle and M. Ranzato and R. Hadsell and M.F. Balcan and H. Lin},
	pages = {7308--7320},
	publisher = {Curran Associates, Inc.},
	title = {Understanding the Role of Training Regimes in Continual Learning},
	url ={https://proceedings.neurips.cc/paper_files/paper/2020/file/518a38cc9a0173d0b2dc088166981cf8-Paper.pdf},
	volume = {33},
	year = {2020}
}

@misc{mirzadeh2020linear,
      title={Linear Mode Connectivity in Multitask and Continual Learning}, 
      author={Seyed Iman Mirzadeh and Mehrdad Farajtabar and Dilan Gorur and Razvan Pascanu and Hassan Ghasemzadeh},
      year={2020},
      eprint={2010.04495},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}

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