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Deep neural networks are susceptible to catastrophic forgetting when trained on sequential tasks.
On tiny episodic memories in continual learning
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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 · 2017
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Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms
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Memory aware synapses: Learning what (not) to forget
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Fix your classifier: the marginal value of training the last weight layer
Hoffer, E.; Hubara, I.; and Soudry, D. 2018 · 2018
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Progress & compress: A scalable framework for continual learning
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Overcoming catastrophic forgetting with hard attention to the task
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Reinforced continual learning
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Lifelong learning with dynamically expandable networks
Yoon, J.; Yang, E.; Lee, J.; and Hwang, S. J. 2018 · 2018
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Stochastic configuration networks with block increments for data modeling in process industries
Dai, W.; Li, D.; Zhou, P.; and Chai, T. 2019 · 2019
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Hyperspherical prototype networks
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On variational bounds of mutual information
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Random path selection for incremental learning
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Large scale incremental learning
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Continual learning of context-dependent processing in neural networks
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Dark experience for general continual learning: a strong, simple baseline
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Orthogonal gradient descent for continual learning
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Remind your neural network to prevent catastrophic forgetting
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DER: Dynamically expandable representation for class incremental learning
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On the Effectiveness of Lipschitz-Driven Rehearsal in Continual Learning
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Class-incremental continual learning into the extended der-verse
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Adaptive orthogonal projection for batch and online continual learning
Guo, Y.; Hu, W.; Zhao, D.; and Liu, B. 2022 · 2022
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Online continual learning through mutual information maximization
Guo, Y.; Liu, B.; and Zhao, D. 2022 · 2022
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Forget-free continual learning with winning subnetworks
Kang, H.; Mina, R. J. L.; Madjid, S. R. H.; Yoon, J.; Hasegawa-Johnson, M.; Hwang, S. J.; and Yoo, C. D. 2022 · 2022
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Mnemonics training: Multi-class incremental learning without forgetting
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The HSIC bottleneck: Deep learning without back-propagation
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Class-incremental learning via deep model consolidation
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DualPrompt: Complementary prompting for rehearsal-free continual learning
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Continual learning with guarantees via weight interval constraints
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ACIL: Analytic class-incremental learning with absolute memorization and privacy protection
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Dense network expansion for class incremental learning
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How Does Information Bottleneck Help Deep Learning?
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On the Stability-Plasticity Dilemma of Class-Incremental Learning
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Class-incremental exemplar compression for class-incremental learning
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Better generative replay for continual federated learning
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Equiangular Basis Vectors
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CODA-Prompt: COntinual Decomposed Attention-based Prompting for Rehearsal-Free Continual Learning
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Incremental learning of structured memory via closed-loop transcription
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DualHSIC: HSIC-Bottleneck and Alignment for Continual Learning
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A model or 603 exemplars: Towards memory-efficient class-incremental learning
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