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In class-incremental learning, an agent with limited resources needs to learn a sequence of classification tasks, forming an ever growing classification problem, with the constraint of not being able to access data from previous tasks.
Is learning the n-th thing any easier than learning the first?
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Shin, H., Lee, J. K., Kim, J., and Kim, J · 2017
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Castro, F. M., Marín-Jiménez, M. J., Guil, N., Schmid, C., and Alahari, K · 2018
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Lee, K., Lee, K., Shin, J., and Lee, H · 2019
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Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., and Fu, Y · 2019
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Belouadah, E., Popescu, A., and Kanellos, I · 2020
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Riemannian walk for incremental learning: Understanding forgetting and intransigence
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Overcoming catastrophic forgetting with hard attention to the task
Serra, J., Suris, D., Miron, M., and Karatzoglou, A · 2018
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Wu, C., Herranz, L., Liu, X., Wang, Y., van de Weijer, J., and Raducanu, B · 2018
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Dhar, P., Singh, R. V., Peng, K.-C., 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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Prabhu, A., Torr, P., and Dokania, P · 2020
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Semantic drift compensation for class-incremental learning
Yu, L., Twardowski, B., Liu, X., Herranz, L., Wang, K., Cheng, Y., Jui, S., and Weijer, J. v. d · 2020
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Class-incremental learning via deep model consolidation
Zhang, J., Zhang, J., Ghosh, S., Li, D., Tasci, S., Heck, L., Zhang, H., and Kuo, C.-C. J · 2020
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A continual learning survey: Defying forgetting in classification tasks
Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., and Tuytelaars, T · 2021
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Probing representation forgetting in supervised and unsupervised continual learning
Davari, M., Asadi, N., Mudur, S., Aljundi, R., and Belilovsky, E · 2022
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No reason for no supervision: Improved generalization in supervised models
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