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Humans can incrementally learn to do new visual detection tasks, which is a huge challenge for today's computer vision systems.
Using fast weights to deblur old memories
Geoffrey E Hinton and David C Plaut · 1987
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Catastrophic interference in connectionist networks: The sequential learning problem
Michael McCloskey and Neal J Cohen · 1989
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Self-improving reactive agents based on reinforcement learning, planning and teaching
Long-Ji Lin · 1992
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Why there are complementary learning systems in the hippocampus and neocortex: insights from the successes and failures of connectionist models of learning and memory
James L McClelland, Bruce L McNaughton, and Randall C O’reilly · 1995
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Catastrophic forgetting in connectionist networks
Robert M French · 1999
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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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Mining concept-drifting data streams using ensemble classifiers
Haixun Wang, Wei Fan, Philip S Yu, and Jiawei Han · 2003
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Memory retention–the synaptic stability versus plasticity dilemma
Wickliffe C Abraham and Anthony Robins · 2005
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Boosting for transfer learning
Wenyuan Dai, Qiang Yang, Gui-Rong Xue, and Yong Yu · 2007
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The hippocampal indexing theory and episodic memory: updating the index
Timothy J Teyler and Jerry W Rudy · 2007
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The pascal visual object classes (voc) challenge
Mark Everingham, Luc Van Gool, Christopher KI Williams, John Winn, and Andrew Zisserman · 2010
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Product quantization for nearest neighbor search
Herve Jegou, Matthijs Douze, and Cordelia Schmid · 2010
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Ensemble learning in fixed expansion layer networks for mitigating catastrophic forgetting
Robert Coop, Aaron Mishtal, and Itamar Arel · 2013
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Microsoft coco: Common objects in context
Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollár, and C Lawrence Zitnick · 2014
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How transferable are features in deep neural networks?
Jason Yosinski, Jeff Clune, Yoshua Bengio, and Hod Lipson · 2014
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Edge boxes: Locating object proposals from edges
C Lawrence Zitnick and Piotr Dollár · 2014
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Distilling the knowledge in a neural network
Geoffrey Hinton, Oriol Vinyals, and Jeff Dean · 2015
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Faster r-cnn: Towards real-time object detection with region proposal networks
Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun · 2015
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Learning without forgetting
Zhizhong Li and Derek Hoiem · 2016
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Ssd: Single shot multibox detector
Wei Liu, Dragomir Anguelov, Dumitru Erhan, Christian Szegedy, Scott Reed, Cheng-Yang Fu, and Alexander C Berg · 2016
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You only look once: Unified, real-time object detection
Joseph Redmon, Santosh Divvala, Ross Girshick, and Ali Farhadi · 2016
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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
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Dssd: Deconvolutional single shot detector
Cheng-Yang Fu, Wei Liu, Ananth Ranga, Ambrish Tyagi, and Alexander C Berg · 2017
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Billion-scale similarity search with gpus
Jeff Johnson, Matthijs Douze, and Hervé Jégou · 2017
Shufflenet v2: Practical guidelines for efficient cnn architecture design
Ningning Ma, Xiangyu Zhang, Hai-Tao Zheng, and Jian Sun · 2018
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Continuous learning in single-incremental-task scenarios
Davide Maltoni and Vincenzo Lomonaco · 2018
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Variational continual learning
Cuong V. Nguyen, Yingzhen Li, Thang D. Bui, and Richard E. Turner · 2018
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Mobilenetv2: Inverted residuals and linear bottlenecks
Mark Sandler, Andrew Howard, Menglong Zhu, Andrey Zhmoginov, and Liang-Chieh Chen · 2018
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Incremental deep learning for robust object detection in unknown cluttered environments
Dong Kyun Shin, Minhaz Uddin Ahmed, and Phil Kyu Rhee · 2018
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VQD: Visual query detection in natural scenes
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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, Demis Hassabis, Claudia Clopath, Dharshan Kumaran, and Raia Hadsell · 2017
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Focal loss for dense object detection
Tsung-Yi Lin, Priya Goyal, Ross Girshick, Kaiming He, and Piotr Dollár · 2017
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Gradient episodic memory for continual learning
David Lopez-Paz and Marc’Aurelio Ranzato · 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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Yolo9000: better, faster, stronger
Joseph Redmon and Ali Farhadi · 2017
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Life-long learning based on dynamic combination model
Boya Ren, Hongzhi Wang, Jianzhong Li, and Hong Gao · 2017
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Incremental learning of object detectors without catastrophic forgetting
Konstantin Shmelkov, Cordelia Schmid, and Karteek Alahari · 2017
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Manoj Acharya, Karan Jariwala, and Christopher Kanan · 2019
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Efficient lifelong learning with a-GEM
Arslan Chaudhry, Marc’Aurelio Ranzato, Marcus Rohrbach, and Mohamed Elhoseiny · 2019
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An end-to-end architecture for class-incremental object detection with knowledge distillation
Yu Hao, Yanwei Fu, Yu-Gang Jiang, and Qi Tian · 2019
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Memory efficient experience replay for streaming learning
Tyler L Hayes, Nathan D Cahill, and Christopher Kanan · 2019
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Learning a unified classifier incrementally via rebalancing
Saihui Hou, Xinyu Pan, Chen Change Loy, Zilei Wang, and Dahua Lin · 2019
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Augmentation for small object detection
Mate Kisantal, Zbigniew Wojna, Jakub Murawski, Jacek Naruniec, and Kyunghyun Cho · 2019
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Rilod: near real-time incremental learning for object detection at the edge
Dawei Li, Serafettin Tasci, Shalini Ghosh, Jingwen Zhu, Junting Zhang, and Larry Heck · 2019
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Fine-grained continual learning
Vincenzo Lomonaco, Davide Maltoni, and Lorenzo Pellegrini · 2019
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Continual lifelong learning with neural networks: A review
German I Parisi, Ronald Kemker, Jose L Part, Christopher Kanan, and Stefan Wermter · 2019
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Data augmentation for object detection via progressive and selective instance-switching
Hao Wang, Qilong Wang, Fan Yang, Weiqi Zhang, and Wangmeng Zuo · 2019
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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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Learning data augmentation strategies for object detection
Barret Zoph, Ekin D Cubuk, Golnaz Ghiasi, Tsung-Yi Lin, Jonathon Shlens, and Quoc V Le · 2019
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Modeling the background for incremental learning in semantic segmentation
Fabio Cermelli, Massimiliano Mancini, Samuel Rota Bulo, Elisa Ricci, and Barbara Caputo · 2020
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Lifelong machine learning with deep streaming linear discriminant analysis
Tyler L Hayes and Christopher Kanan · 2020
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Remind your neural network to prevent catastrophic forgetting
Tyler L Hayes, Kushal Kafle, Robik Shrestha, Manoj Acharya, and Christopher Kanan · 2020
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