Fetching the paper…
Reading the bibliography…
We propose a general, yet simple patch that can be applied to existing regularization-based continual learning methods called classifier-projection regularization (CPR).
Sanov property, generalized I-projection and a conditional limit theorem
Imre Csiszár · 1984
Earlier work this paper cites.
Art 2: Self-organization of stable category recognition codes for analog input patterns
Gail A Carpenter and Stephen Grossberg · 1987
Earlier work this paper cites.
Catastrophic interference in connectionist networks: The sequential learning problem
Michael McCloskey and Neal J Cohen · 1989
Earlier work this paper cites.
Gradient-based learning applied to document recognition
Yann LeCun, Léon Bottou, Yoshua Bengio, and Patrick Haffner · 1998
Earlier work this paper cites.
Information geometry of-projection in mean field approximation
Shun-ichi Amari, S Ikeda, and H Shimokawa · 2001
Earlier work this paper cites.
Information projections revisited
Imre Csiszár and Frantisek Matus · 2003
Earlier work this paper cites.
Information theory and statistics: A tutorial
Imre Csiszár and Paul C Shields · 2004
Earlier work this paper cites.
Learning multiple layers of features from tiny images
Alex Krizhevsky, Geoffrey Hinton, et al · 2009
Earlier work this paper cites.
Belief propagation, dykstra’s algorithm, and iterated information projections
John MacLaren Walsh and Phillip A Regalia · 2010
Earlier work this paper cites.
Caltech-ucsd birds 200
Peter Welinder, Steve Branson, Takeshi Mita, Catherine Wah, Florian Schroff, Serge Belongie, and Pietro Perona · 2010
Earlier work this paper cites.
Elements of information theory
Thomas M Cover and Joy A Thomas · 2012
Earlier work this paper cites.
Machine learning: a probabilistic perspective
Kevin P Murphy · 2012
Earlier work this paper cites.
The stability-plasticity dilemma: Investigating the continuum from catastrophic forgetting to age-limited learning effects
Martial Mermillod, Aurélia Bugaiska, and Patrick Bonin · 2013
Earlier work this paper cites.
Human-level concept learning through probabilistic program induction
Brenden M Lake, Ruslan Salakhutdinov, and Joshua B Tenenbaum · 2015
Earlier work this paper cites.
Greg Brockman, Vicki Cheung, Ludwig Pettersson, Jonas Schneider, John Schulman, Jie Tang, and Wojciech Zaremba · 2016
Cited alongside, same era.
Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
Cited alongside, same era.
Andrei A Rusu, Neil C Rabinowitz, Guillaume Desjardins, Hubert Soyer, James Kirkpatrick, Koray Kavukcuoglu, Razvan Pascanu, and Raia Hadsell · 2016
Cited alongside, same era.
Rethinking the inception architecture for computer vision
Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jon Shlens, and Zbigniew Wojna · 2016
Cited alongside, same era.
On large-batch training for deep learning: Generalization gap and sharp minima
Nitish Shirish Keskar, Dheevatsa Mudigere, Jorge Nocedal, Mikhail Smelyanskiy, and Ping Tak Peter Tang · 2017
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
Later among the works it cites.
Fearnet: Brain-inspired model for incremental learning
Ronald Kemker and Christopher Kanan · 2018
Later among the works it cites.
Umap: Uniform manifold approximation and projection for dimension reduction
Leland McInnes, John Healy, and James Melville · 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.
Lifelong learning with dynamically expandable networks
Jaehong Yoon, Eunho Yang, Jeongtae Lee, and Sung Ju Hwang · 2018
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
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, Demis Hassabis, Claudia Clopath, Dharshan Kumaran, and Raia Hadsell · 2017
Cited alongside, same era.
Learning without forgetting
Zhizhong Li and Derek Hoiem · 2017
Cited alongside, same era.
Gradient episodic memory for continual learning
David Lopez-Paz and Marc Aurelio Ranzato · 2017
Cited alongside, same era.
Regularizing neural networks by penalizing confident output distributions
Gabriel Pereyra, George Tucker, Jan Chorowski, Łukasz Kaiser, and Geoffrey Hinton · 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.
Proximal policy optimization algorithms
John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov · 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.
Ying Zhang, Tao Xiang, Timothy M Hospedales, and Huchuan Lu · 2018
Later among the works it cites.
Uncertainty-based continual learning with adaptive regularization
Hongjoon Ahn, Sungmin Cha, Donggyu Lee, and Taesup Moon · 2019
Later among the works it cites.
Selfless sequential learning
Rahaf Aljundi, Marcus Rohrbach, and Tinne Tuytelaars · 2019
Later among the works it cites.
Entropy-sgd: Biasing gradient descent into wide valleys
Pratik Chaudhari, Anna Choromanska, Stefano Soatto, Yann LeCun, Carlo Baldassi, Christian Borgs, Jennifer Chayes, Levent Sagun, and Riccardo Zecchina · 2019
Later among the works it cites.
Continual learning via neural pruning
Siavash Golkar, Michael Kagan, and Kyunghyun Cho · 2019
Later among the works it cites.
Continual lifelong learning with neural networks: A review
German I Parisi, Ronald Kemker, Jose L Part, Christopher Kanan, and Stefan Wermter · 2019
Later among the works it cites.
Pyhessian: Neural networks through the lens of the hessian
Zhewei Yao, Amir Gholami, Kurt Keutzer, and Michael Mahoney · 2019
Later among the works it cites.
Continual learning with node-importance based adaptive group sparse regularization
Sangwon Jung, Hongjoon Ahn, Sungmin Cha, and Taesup Moon · 2020
Closest in time.
Understanding the role of training regimes in continual learning
Seyed Iman Mirzadeh, Mehrdad Farajtabar, Razvan Pascanu, and Hassan Ghasemzadeh · 2020
Closest in time.