Fetching the paper…
Reading the bibliography…
Lifelong or continual learning remains to be a challenge for artificial neural network, as it is required to be both stable for preservation of old knowledge and plastic for acquisition of new knowledge.
Catastrophic interference in connectionist networks
M. Mccloskey · 1989
Earlier work this paper cites.
Catastrophic interference in connectionist networks: Can it be predicted, can it be prevented?
Robert M. French · 1993
Earlier work this paper cites.
Catastrophic forgetting in neural networks: the role of rehearsal mechanisms
Anthony Robins · 1993
Earlier work this paper cites.
Hippocampal and neocortical contributions to memory: advances in the complementary learning systems framework
Randall, C, O’Reilly, , , Kenneth, A, and Norman · 2002
Earlier work this paper cites.
Memory retention – the synaptic stability versus plasticity dilemma
Wickliffe C. Abraham and Anthony Robins · 2005
Earlier work this paper cites.
Ensemble learning in fixed expansion layer networks for mitigating catastrophic forgetting
Robert Coop, Aaron Mishtal, and Itamar Arel · 2013
Earlier work this paper cites.
Creating a false memory in the hippocampus
Steve Ramirez, Xu Liu, Pei-Ann Lin, Junghyup Suh, Michele Pignatelli, Roger L Redondo, Tomás J Ryan, and Susumu Tonegawa · 2013
Earlier work this paper cites.
Generative adversarial nets
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2014
Earlier work this paper cites.
Unsupervised neuron selection for mitigating catastrophic forgetting in neural networks
Ben Goodrich and Itamar Arel · 2014
Earlier work this paper cites.
Unsupervised visual representation learning by context prediction
Carl Doersch, Abhinav Gupta, and Alexei A Efros · 2015
Earlier work this paper cites.
Discriminative unsupervised feature learning with exemplar convolutional neural networks
Alexey Dosovitskiy, Philipp Fischer, Jost Tobias Springenberg, Martin Riedmiller, and Thomas Brox · 2015
Earlier work this paper cites.
A bio-inspired incremental learning architecture for applied perceptual problems
Alexander Gepperth and Cem Karaoguz · 2016
Earlier work this paper cites.
What learning systems do intelligent agents need? complementary learning systems theory updated
Dharshan Kumaran, Demis Hassabis, and James L. Mcclelland · 2016
Earlier work this paper cites.
Learning without forgetting
Zhizhong Li and Derek Hoiem · 2016
Earlier work this paper cites.
Unsupervised learning of visual representations by solving jigsaw puzzles
Mehdi Noroozi and Paolo Favaro · 2016
Earlier work this paper cites.
icarl: Incremental classifier and representation learning
Sylvestre Alvise Rebuffi, Alexander Kolesnikov, Georg Sperl, and Christoph H Lampert · 2016
Earlier work this paper cites.
Progressive neural networks
Andrei A Rusu, Neil C Rabinowitz, Guillaume Desjardins, Hubert Soyer, James Kirkpatrick, Koray Kavukcuoglu, Razvan Pascanu, and Raia Hadsell · 2016
Earlier work this paper cites.
Pathnet: Evolution channels gradient descent in super neural networks
Chrisantha Fernando, Dylan Banarse, Charles Blundell, Yori Zwols, David Ha, Andrei A Rusu, Alexander Pritzel, and Daan Wierstra · 2017
Cited alongside, same era.
Deep generative dual memory network for continual learning
Nitin Kamra, Umang Gupta, and Yan Liu · 2017
Cited alongside, same era.
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, and Agnieszka Grabska-Barwinska · 2017
Cited alongside, same era.
Colorization as a proxy task for visual understanding
Gustav Larsson, Michael Maire, and Gregory Shakhnarovich · 2017
Cited alongside, same era.
Lifelong learning with dynamically expandable networks
Jeongtae. Lee, Jaehong. Yun, Sungju. Hwang, and Eunho. Yang · 2017
Cited alongside, same era.
Gradient episodic memory for continual learning
Representation learning with contrastive predictive coding
Aaron van den Oord, Yazhe Li, and Oriol Vinyals · 2018
Later among the works it cites.
Overcoming catastrophic forgetting with hard attention to the task
Joan Serrà, Dídac Surís, Marius Miron, and Alexandros Karatzoglou · 2018
Later among the works it cites.
Memory replay gans: learning to generate images from new categories without forgetting
Chenshen Wu, Luis Herranz, Xialei Liu, Yaxing Wang, Joost van de Weijer, and Bogdan Raducanu · 2018
Later among the works it cites.
Self-supervised gans via auxiliary rotation loss
Ting Chen, Xiaohua Zhai, Marvin Ritter, Mario Lucic, and Neil Houlsby · 2019
Later among the works it cites.
Data-efficient image recognition with contrastive predictive coding
Olivier J Hénaff, Aravind Srinivas, Jeffrey De Fauw, Ali Razavi, Carl Doersch, SM Eslami, and Aaron van den Oord · 2019
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
David Lopez-Paz and Marc’Aurelio Ranzato · 2017
Cited alongside, same era.
Variational continual learning
Cuong V. Nguyen, Yingzhen Li, Thang D. Bui, and Richard E. Turner · 2017
Cited alongside, same era.
Continual learning with deep generative replay
Hanul Shin, Jung Kwon Lee, Jaehong Kim, and Jiwon Kim · 2017
Cited alongside, same era.
Continual learning through synaptic intelligence
Friedemann Zenke, Ben Poole, and Surya Ganguli · 2017
Cited alongside, same era.
Memory aware synapses: Learning what (not) to forget
Rahaf Aljundi, Francesca Babiloni, Mohamed Elhoseiny, Marcus Rohrbach, and Tinne Tuytelaars · 2018
Cited alongside, same era.
Unsupervised representation learning by predicting image rotations
Spyros Gidaris, Praveer Singh, and Nikos Komodakis · 2018
Cited alongside, same era.
Overcoming catastrophic interference using conceptor-aided backpropagation
Xu He and Herbert Jaeger · 2018
Cited alongside, same era.
Using self-supervised learning can improve model robustness and uncertainty
Dan Hendrycks, Mantas Mazeika, Saurav Kadavath, and Dawn Song · 2019
Later among the works it cites.
Rethinking data augmentation: Self-supervision and self-distillation
Hankook Lee, Sung Ju Hwang, and Jinwoo Shin · 2019
Later among the works it cites.
Generative models from the perspective of continual learning
Timothee Lesort, Hugo Caselles-Dupre, Michael Garcia-Ortiz, Andrei Stoian, and David Filliat · 2019
Later among the works it cites.
Learning to remember: A synaptic plasticity driven framework for continual learning
Oleksiy Ostapenko, Mihai Puscas, Tassilo Klein, Patrick Jahnichen, and Moin Nabi · 2019
Later among the works it cites.
Continual unsupervised representation learning
Dushyant Rao, Francesco Visin, Andrei Rusu, Razvan Pascanu, Yee Whye Teh, and Raia Hadsell · 2019
Later among the works it cites.
Unsupervised progressive learning and the stam architecture
James Smith and Constantine Dovrolis · 2019
Later among the works it cites.
Three scenarios for continual learning
Gido M van de Ven and Andreas S Tolias · 2019
Later among the works it cites.
Continual learning of context-dependent processing in neural networks
Guanxiong Zeng, Yang Chen, Bo Cui, and Shan Yu · 2019
Later among the works it cites.
S4l: Self-supervised semi-supervised learning
Xiaohua Zhai, Avital Oliver, Alexander Kolesnikov, and Lucas Beyer · 2019
Later among the works it cites.
Aet vs. aed: Unsupervised representation learning by auto-encoding transformations rather than data
Liheng Zhang, Guo-Jun Qi, Liqiang Wang, and Jiebo Luo · 2019
Later among the works it cites.
Generative feature replay with orthogonal weight modification for continual learning
Gehui Shen, Song Zhang, Xiang Chen, and Zhi-Hong Deng · 2020
Closest in time.