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Modern deep learning approaches have achieved great success in many vision applications by training a model using all available task-specific data.
Catastrophic interference in connectionist networks: The sequential learning problem
Michael McCloskey and Neal J Cohen · 1989
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Support-vector networks
Corinna Cortes and Vladimir Vapnik · 1995
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Incremental and decremental support vector machine learning
Gert Cauwenberghs and Tomaso Poggio · 2001
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Learn++: An incremental learning algorithm for supervised neural networks
Robi Polikar, Lalita Upda, Satish S Upda, and Vasant Honavar · 2001
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Incremental learning with support vector machines
Stefan Ruping · 2001
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Learning multiple layers of features from tiny images
Alex Krizhevsky, Geoffrey Hinton, et al · 2009
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Herding dynamical weights to learn
Max Welling · 2009
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From n to n+ 1: Multiclass transfer incremental learning
Ilja Kuzborskij, Francesco Orabona, and Barbara Caputo · 2013
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Distance-based image classification: Generalizing to new classes at near-zero cost
Thomas Mensink, Jakob Verbeek, Florent Perronnin, and Gabriela Csurka · 2013
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Food-101 – mining discriminative components with random forests
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A survey on concept drift adaptation
João Gama, Indrė Žliobaitė, Albert Bifet, Mykola Pechenizkiy, and Abdelhamid Bouchachia · 2014
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Generative adversarial nets
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2014
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Incremental learning of ncm forests for large-scale image classification
Marko Ristin, Matthieu Guillaumin, Juergen Gall, and Luc Van Gool · 2014
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Very deep convolutional networks for large scale image recognition
Karen Simonyan and Andrew Zisserman · 2014
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Distilling the knowledge in a neural network
Geoffrey Hinton, Oriol Vinyals, and Jeffrey Dean · 2015
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Classifier adaptation at prediction time
Amelie Royer and Christoph H Lampert · 2015
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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, Alexander C. Berg, and Li Fei-Fei · 2015
Learning without forgetting
Zhizhong Li and Derek Hoiem · 2017
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Automatic differentiation in PyTorch
Adam Paszke, Sam Gross, Soumith Chintala, Gregory Chanan, Edward Yang, Zachary DeVito, Zeming Lin, Alban Desmaison, Luca Antiga, and Adam Lerer · 2017
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Encoder based lifelong learning
Amal Rannen, Rahaf Aljundi, Matthew B Blaschko, and Tinne Tuytelaars · 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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A strategy for an uncompromising incremental learner
Ragav Venkatesan, Hemanth Venkateswara, Sethuraman Panchanathan, and Baoxin Li · 2017
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Less-forgetting learning in deep neural networks
Heechul Jung, Jeongwoo Ju, Minju Jung, and Junmo Kim · 2016
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Characterizing concept drift
Geoffrey I Webb, Roy Hyde, Hong Cao, Hai Long Nguyen, and Francois Petitjean · 2016
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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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End-to-end incremental learning
Francisco M. Castro, Manuel J. Marin-Jimenez, Nicolas Guil, Cordelia Schmid, and Karteek Alahari · 2018
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Deep nearest class mean classifiers
Samantha Guerriero, Barbara Caputo, and Thomas Mensink · 2018
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Incremental on-line learning: A review and comparison of state-of-the-art algorithms
Viktor Losing, Barbara Hammer, and Heiko Wersing · 2018
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Large scale incremental learning
Yue Wu, Yinpeng Chen, Lijuan Wang, Yuancheng Ye, Zicheng Liu, Yandong Guo, and Yun Fu · 2019
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