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It has been observed that neural networks perform poorly when the data or tasks are presented sequentially.
How does a brain build a cognitive code?
Stephen Grossberg · 1982
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Computer generation of distributions on the m-sphere
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The Shapley value: essays in honor of Lloyd S. Shapley
Alvin E Roth · 1988
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Why there are complementary learning systems in the hippocampus and neocortex: insights from the successes and failures of connectionist models of learning and memory
James L McClelland, Bruce L McNaughton, and Randall C O’Reilly · 1995
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Flat minima
Sepp Hochreiter and Jürgen Schmidhuber · 1997
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Catastrophic forgetting in connectionist networks
Robert M French · 1999
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Hippocampal and neocortical contributions to memory: Advances in the complementary learning systems framework
Randall C O’Reilly and Kenneth A Norman · 2002
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Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
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Learning multiple layers of features from tiny images
Alex Krizhevsky et al · 2009
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Herding dynamical weights to learn
Max Welling · 2009
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Neural dynamics of saccadic and smooth pursuit eye movement coordination during visual tracking of unpredictably moving targets
Stephen Grossberg, Krishna Srihasam, and Daniel Bullock · 2012
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Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton · 2012
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Intriguing properties of neural networks
Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian Goodfellow, and Rob Fergus · 2013
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Complementary learning systems
Randall C O’Reilly, Rajan Bhattacharyya, Michael D Howard, and Nicholas Ketz · 2014
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Dropout: a simple way to prevent neural networks from overfitting
Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov · 2014
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Learning in nonstationary environments: A survey
Gregory Ditzler, Manuel Roveri, Cesare Alippi, and Robi Polikar · 2015
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Distilling the knowledge in a neural network
Geoffrey Hinton, Oriol Vinyals, Jeff Dean, et al · 2015
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Large-margin softmax loss for convolutional neural networks
Weiyang Liu, Yandong Wen, Zhiding Yu, and Meng Yang · 2016
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Andrei A Rusu, Neil C Rabinowitz, Guillaume Desjardins, Hubert Soyer, James Kirkpatrick, Koray Kavukcuoglu, Razvan Pascanu, and Raia Hadsell · 2016
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Mastering the game of go with deep neural networks and tree search
David Silver, Aja Huang, Chris J Maddison, Arthur Guez, Laurent Sifre, George Van Den Driessche, Julian Schrittwieser, Ioannis Antonoglou, Veda Panneershelvam, Marc Lanctot, et al · 2016
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Neuroscience-inspired artificial intelligence
Demis Hassabis, Dharshan Kumaran, Christopher Summerfield, and Matthew Botvinick · 2017
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Overcoming catastrophic forgetting in neural networks
James Kirkpatrick, Razvan Pascanu, Neil Rabinowitz, Joel Veness, Guillaume Desjardins, Andrei A Rusu, Kieran Milan, John Quan, Tiago Ramalho, Agnieszka Grabska-Barwinska, et al · 2017
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Learning without forgetting
Zhizhong Li and Derek Hoiem · 2017
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Focal loss for dense object detection
Tsung-Yi Lin, Priya Goyal, Ross Girshick, Kaiming He, and Piotr Dollár · 2017
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Gradient episodic memory for continual learning
David Lopez-Paz and Marc’Aurelio Ranzato · 2017
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icarl: Incremental classifier and representation learning
Sylvestre-Alvise Rebuffi, Alexander Kolesnikov, Georg Sperl, and Christoph H Lampert · 2017
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Continual learning with deep generative replay
Hanul Shin, Jung Kwon Lee, Jaehong Kim, and Jiwon Kim · 2017
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Prototypical networks for few-shot learning
Jake Snell, Kevin Swersky, and Richard Zemel · 2017
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Neural discrete representation learning
Aaron Van Den Oord, Oriol Vinyals, et al · 2017
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Continual learning through synaptic intelligence
Friedemann Zenke, Ben Poole, and Surya Ganguli · 2017
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Regularizing neural networks via minimizing hyperspherical energy
Rongmei Lin, Weiyang Liu, Zhen Liu, Chen Feng, Zhiding Yu, James M Rehg, Li Xiong, and Le Song · 2020
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Mnemonics training: Multi-class incremental learning without forgetting
Yaoyao Liu, Yuting Su, An-An Liu, Bernt Schiele, and Qianru Sun · 2020
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Understanding the role of training regimes in continual learning
Seyed Iman Mirzadeh, Mehrdad Farajtabar, Razvan Pascanu, and Hassan Ghasemzadeh · 2020
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Prevalence of neural collapse during the terminal phase of deep learning training
Vardan Papyan, XY Han, and David L Donoho · 2020
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Continual learning with hypernetworks
Johannes von Oswald, Christian Henning, Benjamin F Grewe, and João Sacramento · 2020
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Understanding contrastive representation learning through alignment and uniformity on the hypersphere
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Memory aware synapses: Learning what (not) to forget
Rahaf Aljundi, Francesca Babiloni, Mohamed Elhoseiny, Marcus Rohrbach, and Tinne Tuytelaars · 2018
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Large scale gan training for high fidelity natural image synthesis
Andrew Brock, Jeff Donahue, and Karen Simonyan · 2018
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Efficient lifelong learning with a-gem
Arslan Chaudhry, Marc’Aurelio Ranzato, Marcus Rohrbach, and Mohamed Elhoseiny · 2018
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Hyperspherical variational auto-encoders
Tim R Davidson, Luca Falorsi, Nicola De Cao, Thomas Kipf, and Jakub M Tomczak · 2018
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Learning to learn without forgetting by maximizing transfer and minimizing interference
Matthew Riemer, Ignacio Cases, Robert Ajemian, Miao Liu, Irina Rish, Yuhai Tu, and Gerald Tesauro · 2018
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Overcoming catastrophic forgetting with hard attention to the task
Joan Serra, Didac Suris, Marius Miron, and Alexandros Karatzoglou · 2018
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Tongzhou Wang and Phillip Isola · 2020
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Adversarial weight perturbation helps robust generalization
Dongxian Wu, Shu-Tao Xia, and Yisen Wang · 2020
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Semantic drift compensation for class-incremental learning
Lu Yu, Bartlomiej Twardowski, Xialei Liu, Luis Herranz, Kai Wang, Yongmei Cheng, Shangling Jui, and Joost van de Weijer · 2020
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Ss-il: Separated softmax for incremental learning
Hongjoon Ahn, Jihwan Kwak, Subin Lim, Hyeonsu Bang, Hyojun Kim, and Taesup Moon · 2021
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Reducing representation drift in online continual learning
Lucas Caccia, Rahaf Aljundi, Nader Asadi, Tinne Tuytelaars, Joelle Pineau, and Eugene Belilovsky · 2021
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Simcse: Simple contrastive learning of sentence embeddings
Tianyu Gao, Xingcheng Yao, and Danqi Chen · 2021
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Gradient projection memory for continual learning
Gobinda Saha, Isha Garg, and Kaushik Roy · 2021
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Toward causal representation learning
Bernhard Schölkopf, Francesco Locatello, Stefan Bauer, Nan Rosemary Ke, Nal Kalchbrenner, Anirudh Goyal, and Yoshua Bengio · 2021
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Online class-incremental continual learning with adversarial shapley value
Dongsub Shim, Zheda Mai, Jihwan Jeong, Scott Sanner, Hyunwoo Kim, and Jongseong Jang · 2021
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Training networks in null space of feature covariance for continual learning
Shipeng Wang, Xiaorong Li, Jian Sun, and Zongben Xu · 2021
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Der: Dynamically expandable representation for class incremental learning
Shipeng Yan, Jiangwei Xie, and Xuming He · 2021
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Regularizing neural networks via adversarial model perturbation
Yaowei Zheng, Richong Zhang, and Yongyi Mao · 2021
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Prototype augmentation and self-supervision for incremental learning
Fei Zhu, Xu-Yao Zhang, Chuang Wang, Fei Yin, and Cheng-Lin Liu · 2021
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Unifying importance based regularisation methods for continual learning
Frederik Benzing · 2022
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Provable lifelong learning of representations
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Sphereface revived: Unifying hyperspherical face recognition
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Neural collapse under cross-entropy loss
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Architecture matters in continual learning
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Gcr: Gradient coreset based replay buffer selection for continual learning
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Memory replay with data compression for continual learning
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