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Forgetting refers to the loss or deterioration of previously acquired knowledge.
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2018
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2018
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2018
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G. I. Parisi, R. Kemker, J. L. Part, C. Kanan, and S. Wermter, “Continual lifelong learning with neural networks: A review,”
2019
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2019
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K. Lee, K. Lee, J. Shin, and H. Lee, “Overcoming catastrophic forgetting with unlabeled data in the wild,” in
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N. Shoham, T. Avidor, A. Keren, N. Israel, D. Benditkis, L. Mor-Yosef, and I. Zeitak, “Overcoming forgetting in federated learning on non-iid data,”
2019
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K. Audhkhasi, G. Saon, Z. Tüske, B. Kingsbury, and M. Picheny, “Forget a bit to learn better: Soft forgetting for ctc-based automatic speech recognition.” in
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L. Gravitz, “The importance of forgetting,”
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B. Kim, H. Kim, K. Kim, S. Kim, and J. Kim, “Learning not to learn: Training deep neural networks with biased data,” in
2019
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M. Du, Z. Chen, C. Liu, R. Oak, and D. Song, “Lifelong anomaly detection through unlearning,” in
2019
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2019
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2020
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2020
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2020
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2023
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2023
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2023
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2023
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I. Evron, E. Moroshko, G. Buzaglo, M. Khriesh, B. Marjieh, N. Srebro, and D. Soudry, “Continual learning in linear classification on separable data,” in
2023
Closest in time.
L. Peng, P. V. Giampouras, and R. Vidal, “The ideal continual learner: An agent that never forgets,” in
2023
Closest in time.
2023
Closest in time.
M. Jagielski, O. Thakkar, F. Tramer, D. Ippolito, K. Lee, N. Carlini, E. Wallace, S. Song, A. G. Thakurta, N. Papernot, and C. Zhang, “Measuring forgetting of memorized training examples,” in
2023
Closest in time.
Z. Fatemi, C. Xing, W. Liu, and C. Xiong, “Improving gender fairness of pre-trained language models without catastrophic forgetting,” in
2023
Closest in time.
N. Lukas, A. Salem, R. Sim, S. Tople, L. Wutschitz, and S. Zanella-Béguelin, “Analyzing leakage of personally identifiable information in language models,”
2023
Closest in time.
N. Carlini, D. Ippolito, M. Jagielski, K. Lee, F. Tramer, and C. Zhang, “Quantifying memorization across neural language models,” in
2023
Closest in time.
2023
Closest in time.
A. Razdaibiedina, Y. Mao, R. Hou, M. Khabsa, M. Lewis, and A. Almahairi, “Progressive prompts: Continual learning for language models,” in
2023
Closest in time.
2023
Closest in time.
W. Ahmed, P. Morerio, and V. Murino, “Continual source-free unsupervised domain adaptation,”
2023
Closest in time.
S. Niu, J. Wu, Y. Zhang, Z. Wen, Y. Chen, P. Zhao, and M. Tan, “Towards stable test-time adaptation in dynamic wild world,” in
2023
Closest in time.
J. Liang, R. He, and T. Tan, “A comprehensive survey on test-time adaptation under distribution shifts,” 2023
2023
Closest in time.
J. Hong, L. Lyu, J. Zhou, and M. Spranger, “MECTA: Memory-economic continual test-time model adaptation,” in
2023
Closest in time.
M. Döbler, R. A. Marsden, and B. Yang, “Robust mean teacher for continual and gradual test-time adaptation,” in
2023
Closest in time.
2023
Closest in time.
Y. Gan, X. Ma, Y. Lou, Y. Bai, R. Zhang, N. Shi, and L. Luo, “Decorate the newcomers: Visual domain prompt for continual test time adaptation,”
2023
Closest in time.
J. Song, J. Lee, I. S. Kweon, and S. Choi, “Ecotta: Memory-efficient continual test-time adaptation via self-distilled regularization,” in
2023
Closest in time.
D. Brahma and P. Rai, “A probabilistic framework for lifelong test-time adaptation,” in
2023
Closest in time.
G. Patel, K. R. Mopuri, and Q. Qiu, “Learning to retain while acquiring: Combating distribution-shift in adversarial data-free knowledge distillation,” in
2023
Closest in time.
J.-B. Gaya, T. Doan, L. Caccia, L. Soulier, L. Denoyer, and R. Raileanu, “Building a subspace of policies for scalable continual learning,” in
2023
Closest in time.
K. Luo, X. Li, Y. Lan, and M. Gao, “Gradma: A gradient-memory-based accelerated federated learning with alleviated catastrophic forgetting,” in
2023
Closest in time.
D. Qi, H. Zhao, and S. Li, “Better generative replay for continual federated learning,” in
2023
Closest in time.
2023
Closest in time.
2023
Closest in time.
T. Konishi, M. Kurokawa, C. Ono, Z. Ke, G. Kim, and B. Liu, “Parameter-level soft-masking for continual learning,”
2023
Closest in time.
D.-W. Zhou, Q.-W. Wang, H.-J. Ye, and D.-C. Zhan, “A model or 603 exemplars: Towards memory-efficient class-incremental learning,” in
2023
Closest in time.
Z. Hu, Y. Li, J. Lyu, D. Gao, and N. Vasconcelos, “Dense network expansion for class incremental learning,” in
2023
Closest in time.
Y. Wen, D. Tran, and J. Ba, “Batchensemble: an alternative approach to efficient ensemble and lifelong learning,” in
2023
Closest in time.
D.-W. Zhou, Q.-W. Wang, Z.-H. Qi, H.-J. Ye, D.-C. Zhan, and Z. Liu, “Deep class-incremental learning: A survey,”
2024
Closest in time.
Y. Wu, H. Wang, P. Zhao, Y. Zheng, Y. Wei, and L.-K. Huang, “Mitigating catastrophic forgetting in online continual learning by modeling previous task interrelations via pareto optimization,” in
2024
Closest in time.
W. Zhang, Y. Mohamed, B. Ghanem, P. H. Torr, A. Bibi, and M. Elhoseiny, “Continual learning on a diet: Learning from sparsely labeled streams under constrained computation,”
2024
Closest in time.
X. Zhao, H. Wang, W. Huang, and W. Lin, “A statistical theory of regularization-based continual learning,”
2024
Closest in time.
X. Zhang and J. Wu, “Dissecting learning and forgetting in language model finetuning,” in
2024
Closest in time.
S. Kotha, J. M. Springer, and A. Raghunathan, “Understanding catastrophic forgetting in language models via implicit inference,” in
2024
Closest in time.
D. Zhu, Z. Sun, Z. Li, T. Shen, K. Yan, S. Ding, K. Kuang, and C. Wu, “Model tailor: Mitigating catastrophic forgetting in multi-modal large language models,”
2024
Closest in time.
J. Zhao, Z. Deng, D. Madras, J. Zou, and M. Ren, “Learning and forgetting unsafe examples in large language models,” in
2024
Closest in time.
Z. Wang, Y. Li, L. Shen, and H. Huang, “A unified and general framework for continual learning,” in
2024
Closest in time.
J. Qiao, X. Tan, C. Chen, Y. Qu, Y. Peng, Y. Xie
2024
Closest in time.
M. Xiao, Q. Meng, Z. Zhang, D. He, and Z. Lin, “Hebbian learning based orthogonal projection for continual learning of spiking neural networks,”
2024
Closest in time.