Fetching the paper…
Reading the bibliography…
Modern computer vision applications suffer from catastrophic forgetting when incrementally learning new concepts over time.
Catastrophic forgetting, rehearsal and pseudorehearsal
Anthony Robins · 1995
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.
A kernel two-sample test
Arthur Gretton, Karsten M Borgwardt, Malte J Rasch, Bernhard Schölkopf, and Alexander Smola · 2012
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.
Tiny imagenet visual recognition challenge
Ya Le and Xuan Yang · 2015
Earlier work this paper cites.
Inceptionism: Going deeper into neural networks, 2015
Alexander Mordvintsev, Christopher Olah, and Mike Tyka · 2015
Earlier work this paper cites.
Imagenet large scale visual recognition challenge
Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, et al · 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.
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.
Assisting users in a world full of cameras: A privacy-aware infrastructure for computer vision applications
Anupam Das, Martin Degeling, Xiaoyou Wang, Junjue Wang, Norman Sadeh, and Mahadev Satyanarayanan · 2017
Earlier work this paper cites.
Incremental learning with self-organizing maps
Alexander Gepperth and Cem Karaoguz · 2017
Earlier work this paper cites.
Deep generative dual memory network for continual learning
Nitin Kamra, Umang Gupta, and Yan Liu · 2017
Earlier work this paper cites.
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
Earlier work this paper cites.
Learning without forgetting
Zhizhong Li and Derek Hoiem · 2017
Earlier work this paper cites.
Core50: a new dataset and benchmark for continuous object recognition
Vincenzo Lomonaco and Davide Maltoni · 2017
Earlier work this paper cites.
Data-free knowledge distillation for deep neural networks
Raphael Gontijo Lopes, Stefano Fenu, and Thad Starner · 2017
Earlier work this paper cites.
Gradient episodic memory for continual learning
David Lopez-Paz and Marc’Aurelio Ranzato · 2017
Earlier work this paper cites.
icarl: Incremental classifier and representation learning
Sylvestre-Alvise Rebuffi, Alexander Kolesnikov, Georg Sperl, and Christoph H. Lampert · 2017
Earlier work this paper cites.
Continual learning with deep generative replay
Hanul Shin, Jung Kwon Lee, Jaehong Kim, and Jiwon Kim · 2017
Earlier work this paper cites.
Continual learning through synaptic intelligence
Friedemann Zenke, Ben Poole, and Surya Ganguli · 2017
Earlier work this paper cites.
Memory aware synapses: Learning what (not) to forget
Rahaf Aljundi, Francesca Babiloni, Mohamed Elhoseiny, Marcus Rohrbach, and Tinne Tuytelaars · 2018
Earlier work this paper cites.
End-to-end incremental learning
Francisco M Castro, Manuel J Marín-Jiménez, Nicolás Guil, Cordelia Schmid, and Karteek Alahari · 2018
Earlier work this paper cites.
Cloud-based or on-device: An empirical study of mobile deep inference
Tian Guo · 2018
Cited alongside, same era.
Lifelong learning via progressive distillation and retrospection
Saihui Hou, Xinyu Pan, Chen Change Loy, Zilei Wang, and Dahua Lin · 2018
Cited alongside, same era.
Re-evaluating continual learning scenarios: A categorization and case for strong baselines
Yen-Chang Hsu, Yen-Cheng Liu, Anita Ramasamy, and Zsolt Kira · 2018
Cited alongside, same era.
Fearnet: Brain-inspired model for incremental learning
Ronald Kemker and Christopher Kanan · 2018
Cited alongside, same era.
Measuring catastrophic forgetting in neural networks
Ronald Kemker, Marc McClure, Angelina Abitino, Tyler Hayes, and Christopher Kanan · 2018
Cited alongside, same era.
Theoretical insights into memorization in gans
Experience replay for continual learning
David Rolnick, Arun Ahuja, Jonathan Schwarz, Timothy Lillicrap, and Gregory Wayne · 2019
Later among the works it cites.
Functional regularisation for continual learning with gaussian processes
Michalis K Titsias, Jonathan Schwarz, Alexander G de G Matthews, Razvan Pascanu, and Yee Whye Teh · 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 with hypernetworks
Johannes von Oswald, Christian Henning, João Sacramento, and Benjamin F Grewe · 2019
Later among the works it cites.
Large scale incremental learning
Yue Wu, Yinpeng Chen, Lijuan Wang, Yuancheng Ye, Zicheng Liu, Yandong Guo, and Yun Fu · 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…
Vaishnavh Nagarajan, Colin Raffel, and Ian J Goodfellow · 2018
Cited alongside, same era.
Generative replay with feedback connections as a general strategy for continual learning
Gido M van de Ven and Andreas S Tolias · 2018
Cited alongside, same era.
Online continual learning with maximal interfered retrieval
Rahaf Aljundi, Eugene Belilovsky, Tinne Tuytelaars, Laurent Charlin, Massimo Caccia, Min Lin, and Lucas Page-Caccia · 2019
Cited alongside, same era.
Gradient based sample selection for online continual learning
Rahaf Aljundi, Min Lin, Baptiste Goujaud, and Yoshua Bengio · 2019
Cited alongside, same era.
Privacy-preserving generative deep neural networks support clinical data sharing
Brett K Beaulieu-Jones, Zhiwei Steven Wu, Chris Williams, Ran Lee, Sanjeev P Bhavnani, James Brian Byrd, and Casey S Greene · 2019
Cited alongside, same era.
Efficient lifelong learning with a-GEM
Arslan Chaudhry, Marc’Aurelio Ranzato, Marcus Rohrbach, and Mohamed Elhoseiny · 2019
Cited alongside, same era.
Continual learning with tiny episodic memories
Arslan Chaudhry, Marcus Rohrbach, Mohamed Elhoseiny, Thalaiyasingam Ajanthan, Puneet K Dokania, Philip HS Torr, and Marc’Aurelio Ranzato · 2019
Cited alongside, same era.
Yogesh Balaji, Mehrdad Farajtabar, Dong Yin, Alex Mott, and Ang Li · 2020
Later among the works it cites.
Gan memory with no forgetting
Yulai Cong, Miaoyun Zhao, Jianqiao Li, Sijia Wang, and Lawrence Carin · 2020
Later among the works it cites.
Adversarial continual learning
Sayna Ebrahimi, Franziska Meier, Roberto Calandra, Trevor Darrell, and Marcus Rohrbach · 2020
Later among the works it cites.
The knowledge within: Methods for data-free model compression
Matan Haroush, Itay Hubara, Elad Hoffer, and Daniel Soudry · 2020
Later among the works it cites.
Automatic recall machines: Internal replay, continual learning and the brain
Xu Ji, Joao Henriques, Tinne Tuytelaars, and Andrea Vedaldi · 2020
Later among the works it cites.
A neural dirichlet process mixture model for task-free continual learning
Soochan Lee, Junsoo Ha, Dongsu Zhang, and Gunhee Kim · 2020
Later among the works it cites.
Bo Liu, Xuesu Xiao, and Peter Stone · 2020
Later among the works it cites.
Large-scale generative data-free distillation
Liangchen Luo, Mark Sandler, Zi Lin, Andrey Zhmoginov, and Andrew Howard · 2020
Later among the works it cites.
Fast and memory efficient de-hazing technique for real-time computer vision applications
Prathap Soma, Ravi Kumar Jatoth, and Hathiram Nenavath · 2020
Later among the works it cites.
Brain-inspired replay for continual learning with artificial neural networks
Gido M van de Ven, Hava T Siegelmann, and Andreas S Tolias · 2020
Later among the works it cites.
Triple memory networks: a brain-inspired method for continual learning
Liyuan Wang, Bo Lei, Qian Li, Hang Su, Jun Zhu, and Yi Zhong · 2020
Later among the works it cites.
Dreaming to distill: Data-free knowledge transfer via deepinversion
Hongxu Yin, Pavlo Molchanov, Jose M Alvarez, Zhizhong Li, Arun Mallya, Derek Hoiem, Niraj K Jha, and Jan Kautz · 2020
Later among the works it cites.
Private-knn: Practical differential privacy for computer vision
Yuqing Zhu, Xiang Yu, Manmohan Chandraker, and Yu-Xiang Wang · 2020
Later among the works it cites.
{EEC}: Learning to encode and regenerate images for continual learning
Ali Ayub and Alan Wagner · 2021
Closest in time.
Memory-efficient semi-supervised continual learning: The world is its own replay buffer
James Smith, Jonathan Balloch, Yen-Chang Hsu, and Zsolt Kira · 2021
Closest in time.
Unsupervised progressive learning and the STAM architecture
James Smith, Cameron Taylor, Seth Baer, and Constantine Dovrolis · 2021
Closest in time.