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This paper studies the challenging continual learning (CL) setting of Class Incremental Learning (CIL).
Catastrophic interference in connectionist networks: The sequential learning problem
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Tiny imagenet visual recognition challenge, 2015
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A baseline for detecting misclassified and out-of-distribution examples in neural networks
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Learning Without Forgetting
Li, Z. and Hoiem, D · 2016
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Rusu, A. A., Rabinowitz, N. C., Desjardins, G., Soyer, H., Kirkpatrick, J., Kavukcuoglu, K., Pascanu, R., and Hadsell, R · 2016
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Expert gate: Lifelong learning with a network of experts
Aljundi, R., Chakravarty, P., and Tuytelaars, T · 2017
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Deep Generative Dual Memory Network for Continual Learning
Kamra, N., Gupta, U., and Liu, Y · 2017
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Overcoming catastrophic forgetting in neural networks
Kirkpatrick, J., Pascanu, R., Rabinowitz, N., Veness, J., Desjardins, G., Rusu, A. A., Milan, K., Quan, J., Ramalho, T., Grabska-Barwinska, A., et al · 2017
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Gradient Episodic Memory for Continual Learning
Lopez-Paz, D. and Ranzato, M · 2017
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PackNet: Adding Multiple Tasks to a Single Network by Iterative Pruning
Mallya, A. and Lazebnik, S · 2017
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iCaRL: Incremental classifier and representation learning
Rebuffi, S.-A., Kolesnikov, A., and Lampert, C. H · 2017
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Continual learning in generative adversarial nets
Seff, A., Beatson, A., Suo, D., and Liu, H · 2017
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Continual learning with deep generative replay
Shin, H., Lee, J. K., Kim, J., and Kim, J · 2017
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Continual learning through synaptic intelligence
Zenke, F., Poole, B., and Ganguli, S · 2017
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End-to-end incremental learning
Castro, F. M., Marín-Jiménez, M. J., Guil, N., Schmid, C., and Alahari, K · 2018
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Lifelong machine learning
Chen, Z. and Liu, B · 2018
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FearNet: Brain-Inspired Model for Incremental Learning
Kemker, R. and Kanan, C · 2018
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Online structured laplace approximations for overcoming catastrophic forgetting
Ritter, H., Botev, A., and Barber, D · 2018
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Progress & compress: A scalable framework for continual learning
Schwarz, J., Luketina, J., Czarnecki, W. M., Grabska-Barwinska, A., Teh, Y. W., Pascanu, R., and Hadsell, R · 2018
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Overcoming catastrophic forgetting with hard attention to the task
Serrà, J., Surís, D., Miron, M., and Karatzoglou, A · 2018
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Memory replay gans: Learning to generate new categories without forgetting
Wu, C., Herranz, L., Liu, X., van de Weijer, J., Raducanu, B., et al · 2018
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Reinforced continual learning
Xu, J. and Zhu, Z · 2018
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Uncertainty-based continual learning with adaptive regularization
Ahn, H., Cha, S., Lee, D., and Moon, T · 2019
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Online continual learning with maximal interfered retrieval
Aljundi, R., Belilovsky, E., Tuytelaars, T., Charlin, L., Caccia, M., Lin, M., and Caccia, L · 2019
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Learning without memorizing
Dhar, P., Singh, R. V., Peng, K., Wu, Z., and Chellappa, R · 2019
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Learning a unified classifier incrementally via rebalancing
Hou, S., Pan, X., Loy, C. C., Wang, Z., and Lin, D · 2019
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Continual learning via principal components projection, 2020
Kim, G. and Liu, B · 2020
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Continual learning with hypernetworks
von Oswald, J., Henning, C., Sacramento, J., and Grewe, B. F · 2020
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Supermasks in superposition
Wortsman, M., Ramanujan, V., Liu, R., Kembhavi, A., Rastegari, M., Yosinski, J., and Farhadi, A · 2020
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Co2l: Contrastive continual learning
Cha, H., Lee, J., and Shin, J · 2021
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Using hindsight to anchor past knowledge in continual learning
Chaudhry, A., Gordo, A., Dokania, P., Torr, P., and Lopez-Paz, D · 2021
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Posterior meta-replay for continual learning
Henning, C., Cervera, M., D’Angelo, F., Von Oswald, J., Traber, R., Ehret, B., Kobayashi, S., Grewe, B. F., and Sacramento, J · 2021
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Parameter-efficient transfer learning for nlp
Houlsby, N., Giurgiu, A., Jastrzebski, S., Morrone, B., De Laroussilhe, Q., Gesmundo, A., Attariyan, M., and Gelly, S · 2019
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Overcoming catastrophic forgetting for continual learning via model adaptation
Hu, W., Lin, Z., Liu, B., Tao, C., Tao, Z., Ma, J., Zhao, D., and Yan, R · 2019
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Compacting, picking and growing for unforgetting continual learning
Hung, C.-Y., Tu, C.-H., Wu, C.-E., Chen, C.-H., Chan, Y.-M., and Chen, C.-S · 2019
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Overcoming catastrophic forgetting with unlabeled data in the wild
Lee, K., Lee, K., Shin, J., and Lee, H · 2019
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Learning to remember: A synaptic plasticity driven framework for continual learning
Ostapenko, O., Puscas, M., Klein, T., Jahnichen, P., and Nabi, M · 2019
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Experience replay for continual learning
Rolnick, D., Ahuja, A., Schwarz, J., Lillicrap, T. P., and Wayne, G · 2019
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Achieving forgetting prevention and knowledge transfer in continual learning
Ke, Z., Liu, B., Ma, N., Xu, H., and Shu, L · 2021
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Continual learning in the teacher-student setup: Impact of task similarity
Lee, S., Goldt, S., and Saxe, A · 2021
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Adaptive aggregation networks for class-incremental learning
Liu, Y., Schiele, B., and Sun, Q · 2021
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Continual learning via local module composition
Ostapenko, O., Rodriguez, P., Caccia, M., and Charlin, L · 2021
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Learning transferable visual models from natural language supervision
Radford, A., Kim, J. W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J., et al · 2021
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Training data-efficient image transformers & distillation through attention
Touvron, H., Cord, M., Douze, M., Massa, F., Sablayrolles, A., and Jégou, H · 2021
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Der: Dynamically expandable representation for class incremental learning
Yan, S., Xie, J., and He, X · 2021
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Prototype augmentation and self-supervision for incremental learning
Zhu, F., Zhang, X.-Y., Wang, C., Yin, F., and Liu, C.-L · 2021
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Is out-of-distribution detection learnable?
Fang, Z., Li, Y., Lu, J., Dong, J., Han, B., and Liu, F · 2022
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Online continual learning through mutual information maximization
Guo, Y., Liu, B., and Zhao, D · 2022
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A theoretical study on solving continual learning
Kim, G., Xiao, C., Konishi, T., Ke, Z., and Liu, B · 2022
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Beyond not-forgetting: Continual learning with backward knowledge transfer
Lin, S., Yang, L., Fan, D., and Zhang, J · 2022
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Continual learning with foundation models: An empirical study of latent replay
Ostapenko, O., Lesort, T., Rodríguez, P., Arefin, M. R., Douillard, A., Rish, I., and Charlin, L · 2022
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Class-incremental learning with strong pre-trained models
Wu, T.-Y., Swaminathan, G., Li, Z., Ravichandran, A., Vasconcelos, N., Bhotika, R., and Soatto, S · 2022
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Open-world continual learning: Unifying novelty detection and continual learning
Kim, G., Xiao, C., Konishi, T., Ke, Z., and Liu, B · 2023
Closest in time.
Ai autonomy: Self-initiated open-world continual learning and adaptation
Liu, B., Mazumder, S., Robertson, E., and Grigsby, S · 2023
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