Fetching the paper…
Reading the bibliography…
Lifelong learning with deep neural networks is well-known to suffer from catastrophic forgetting: the performance on previous tasks drastically degrades when learning a new task.
Catastrophic interference in connectionist networks: The sequential learning problem
Michael McCloskey and Neal J Cohen · 1989
Earlier work this paper cites.
Catastrophic forgetting in connectionist networks
Robert M French · 1999
Earlier work this paper cites.
Self-taught learning: transfer learning from unlabeled data
Rajat Raina, Alexis Battle, Honglak Lee, Benjamin Packer, and Andrew Y Ng · 2007
Earlier work this paper cites.
80 million tiny images: A large data set for nonparametric object and scene recognition
Antonio Torralba, Rob Fergus, and William T Freeman · 2008
Earlier work this paper cites.
Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
Earlier work this paper cites.
Learning multiple layers of features from tiny images
Alex Krizhevsky and Geoffrey Hinton · 2009
Earlier work this paper cites.
Distilling the knowledge in a neural network
Geoffrey Hinton, Oriol Vinyals, and Jeff Dean · 2015
Earlier work this paper cites.
Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
Earlier work this paper cites.
The unreasonable effectiveness of noisy data for fine-grained recognition
Jonathan Krause, Benjamin Sapp, Andrew Howard, Howard Zhou, Alexander Toshev, Tom Duerig, James Philbin, and Li Fei-Fei · 2016
Earlier work this paper cites.
Learning without forgetting
Zhizhong Li and Derek Hoiem · 2016
Earlier work this paper cites.
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.
Wide residual networks
Sergey Zagoruyko and Nikos Komodakis · 2016
Earlier work this paper cites.
Expert gate: Lifelong learning with a network of experts
Rahaf Aljundi, Punarjay Chakravarty, and Tinne Tuytelaars · 2017
Earlier work this paper cites.
A downsampled variant of imagenet as an alternative to the cifar datasets
Patryk Chrabaszcz, Ilya Loshchilov, and Frank Hutter · 2017
Earlier work this paper cites.
On calibration of modern neural networks
Chuan Guo, Geoff Pleiss, Yu Sun, and Kilian Q Weinberger · 2017
Earlier work this paper cites.
Mask r-cnn
Kaiming He, Georgia Gkioxari, Piotr Dollár, and Ross Girshick · 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, Agnieszka Grabska-Barwinska, et al · 2017
Cited alongside, same era.
Overcoming catastrophic forgetting by incremental moment matching
Sang-Woo Lee, Jin-Hwa Kim, Jaehyun Jun, Jung-Woo Ha, and Byoung-Tak Zhang · 2017
Cited alongside, same era.
Gradient episodic memory for continual learning
David Lopez-Paz et al · 2017
Cited alongside, same era.
Encoder based lifelong learning
Amal Rannen, Rahaf Aljundi, Matthew B Blaschko, and Tinne Tuytelaars · 2017
Cited alongside, same era.
icarl: Incremental classifier and representation learning
Sylvestre-Alvise Rebuffi, Alexander Kolesnikov, Georg Sperl, and Christoph H Lampert · 2017
Cited alongside, same era.
Keep and learn: Continual learning by constraining the latent space for knowledge preservation in neural networks
Hyo-Eun Kim, Seungwook Kim, and Jaehwan Lee · 2018
Later among the works it cites.
Training confidence-calibrated classifiers for detecting out-of-distribution samples
Kimin Lee, Honglak Lee, Kibok Lee, and Jinwoo Shin · 2018
Later among the works it cites.
Hierarchical novelty detection for visual object recognition
Kibok Lee, Kimin Lee, Kyle Min, Yuting Zhang, Jinwoo Shin, and Honglak Lee · 2018
Later among the works it cites.
Generative models from the perspective of continual learning
Timothée Lesort, Hugo Caselles-Dupré, Michael Garcia-Ortiz, Andrei Stoian, and David Filliat · 2018
Later among the works it cites.
Supportnet: solving catastrophic forgetting in class incremental learning with support data
Yu Li, Zhongxiao Li, Lizhong Ding, Peng Yang, Yuhui Hu, Wei Chen, and Xin Gao · 2018
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Continual learning with deep generative replay
Hanul Shin, Jung Kwon Lee, Jaehong Kim, and Jiwon Kim · 2017
Cited alongside, same era.
Mastering the game of go without human knowledge
David Silver, Julian Schrittwieser, Karen Simonyan, Ioannis Antonoglou, Aja Huang, Arthur Guez, Thomas Hubert, Lucas Baker, Matthew Lai, Adrian Bolton, et al · 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.
End-to-end incremental learning
Francisco M Castro, Manuel J Marín-Jiménez, Nicolás Guil, Cordelia Schmid, and Karteek Alahari · 2018
Cited alongside, same era.
Riemannian walk for incremental learning: Understanding forgetting and intransigence
Arslan Chaudhry, Puneet K Dokania, Thalaiyasingam Ajanthan, and Philip HS Torr · 2018
Cited alongside, same era.
Later among the works it cites.
Exploring the limits of weakly supervised pretraining
Dhruv Mahajan, Ross Girshick, Vignesh Ramanathan, Kaiming He, Manohar Paluri, Yixuan Li, Ashwin Bharambe, and Laurens van der Maaten · 2018
Later among the works it cites.
Piggyback: Adapting a single network to multiple tasks by learning to mask weights
Arun Mallya, Dillon Davis, and Svetlana Lazebnik · 2018
Later among the works it cites.
Spectral normalization for generative adversarial networks
Takeru Miyato, Toshiki Kataoka, Masanori Koyama, and Yuichi Yoshida · 2018
Later among the works it cites.
Variational continual learning
Cuong V Nguyen, Yingzhen Li, Thang D Bui, and Richard E Turner · 2018
Later among the works it cites.
Online structured laplace approximations for overcoming catastrophic forgetting
Hippolyt Ritter, Aleksandar Botev, and David Barber · 2018
Later among the works it cites.
Progress & compress: A scalable framework for continual learning
Jonathan Schwarz, Jelena Luketina, Wojciech M Czarnecki, Agnieszka Grabska-Barwinska, Yee Whye Teh, Razvan Pascanu, and Raia Hadsell · 2018
Later among the works it cites.
Overcoming catastrophic forgetting with hard attention to the task
Joan Serra, Dídac Surís, Marius Miron, and Alexandros Karatzoglou · 2018
Later among the works it cites.
Generative replay with feedback connections as a general strategy for continual learning
Gido M van de Ven and Andreas S Tolias · 2018
Later among the works it cites.
Incremental classifier learning with generative adversarial networks
Yue Wu, Yinpeng Chen, Lijuan Wang, Yuancheng Ye, Zicheng Liu, Yandong Guo, Zhengyou Zhang, and Yun Fu · 2018
Later among the works it cites.
Lifelong learning with dynamically expandable networks
Jaehong Yoon, Eunho Yang, Jeongtae Lee, and Sung Ju Hwang · 2018
Later among the works it cites.