Fetching the paper…
Reading the bibliography…
Real-time on-device continual learning is needed for new applications such as home robots, user personalization on smartphones, and augmented/virtual reality headsets.
Note on a method for calculating corrected sums of squares and products
BP Welford · 1962
Earlier work this paper cites.
Optimal brain damage
Yann LeCun, John Denker, and Sara Solla · 1989
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.
Pseudo-recurrent connectionist networks: An approach to the ‘sensitivity-stability’ dilemma
Robert M French · 1997
Earlier work this paper cites.
Catastrophic forgetting in connectionist networks
Robert M French · 1999
Earlier work this paper cites.
Model compression
Cristian Bucilua, Rich Caruana, and Alexandru Niculescu-Mizil · 2006
Earlier work this paper cites.
Expectation backpropagation: Parameter-free training of multilayer neural networks with continuous or discrete weights
Daniel Soudry, Itay Hubara, and Ron Meir · 2014
Earlier work this paper cites.
Learning both weights and connections for efficient neural network
Song Han, Jeff Pool, John Tran, and William Dally · 2015
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.
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
Earlier work this paper cites.
Data-free parameter pruning for deep neural networks
Suraj Srinivas and R Venkatesh Babu · 2015
Earlier work this paper cites.
Learning the number of neurons in deep networks
Jose M Alvarez and Mathieu Salzmann · 2016
Earlier work this paper cites.
Designing neural network architectures using reinforcement learning
Bowen Baker, Otkrist Gupta, Nikhil Naik, and Ramesh Raskar · 2016
Earlier work this paper cites.
Matthieu Courbariaux, Itay Hubara, Daniel Soudry, Ran El-Yaniv, and Yoshua Bengio · 2016
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.
Network trimming: A data-driven neuron pruning approach towards efficient deep architectures
Hengyuan Hu, Rui Peng, Yu-Wing Tai, and Chi-Keung Tang · 2016
Earlier work this paper cites.
Squeezenet: Alexnet-level accuracy with 50x fewer parameters and¡ 0.5 mb model size
Forrest N Iandola, Song Han, Matthew W Moskewicz, Khalid Ashraf, William J Dally, and Kurt Keutzer · 2016
Earlier work this paper cites.
Minje Kim and Paris Smaragdis · 2016
Earlier work this paper cites.
Pruning filters for efficient convnets
Hao Li, Asim Kadav, Igor Durdanovic, Hanan Samet, and Hans Peter Graf · 2016
Earlier work this paper cites.
Learning without forgetting
Zhizhong Li and Derek Hoiem · 2016
Earlier work this paper cites.
Xnor-net: Imagenet classification using binary convolutional neural networks
Mohammad Rastegari, Vicente Ordonez, Joseph Redmon, and Ali Farhadi · 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.
Matching networks for one shot learning
Oriol Vinyals, Charles Blundell, Timothy Lillicrap, Daan Wierstra, et al · 2016
Earlier work this paper cites.
Quantized convolutional neural networks for mobile devices
Jiaxiang Wu, Cong Leng, Yuhang Wang, Qinghao Hu, and Jian Cheng · 2016
Earlier work this paper cites.
Dorefa-net: Training low bitwidth convolutional neural networks with low bitwidth gradients
Shuchang Zhou, Yuxin Wu, Zekun Ni, Xinyu Zhou, He Wen, and Yuheng Zou · 2016
Earlier work this paper cites.
Neural architecture search with reinforcement learning
Barret Zoph and Quoc V Le · 2016
Earlier work this paper cites.
Mobilenets: Efficient convolutional neural networks for mobile vision applications
Andrew G Howard, Menglong Zhu, Bo Chen, Dmitry Kalenichenko, Weijun Wang, Tobias Weyand, Marco Andreetto, and Hartwig Adam · 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, Demis Hassabis, Claudia Clopath, Dharshan Kumaran, and Raia Hadsell · 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.
Gradient episodic memory for continual learning
David Lopez-Paz and Marc’Aurelio Ranzato · 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.
Prototypical networks for few-shot learning
Jake Snell, Kevin Swersky, and Richard Zemel · 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.
Proxylessnas: Direct neural architecture search on target task and hardware
Han Cai, Ligeng Zhu, and Song Han · 2018
Searching for mobilenetv3
Andrew Howard, Mark Sandler, Grace Chu, Liang-Chieh Chen, Bo Chen, Mingxing Tan, Weijun Wang, Yukun Zhu, Ruoming Pang, Vijay Vasudevan, et al · 2019
Later among the works it cites.
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
Later among the works it cites.
Large-scale long-tailed recognition in an open world
Ziwei Liu, Zhongqi Miao, Xiaohang Zhan, Jiayun Wang, Boqing Gong, and Stella X Yu · 2019
Later among the works it cites.
Learning to remember: A synaptic plasticity driven framework for continual learning
Oleksiy Ostapenko, Mihai Puscas, Tassilo Klein, Patrick Jähnichen, and Moin Nabi · 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.
Efficientnet: Rethinking model scaling for convolutional neural networks
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
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.
Lifelong learning via progressive distillation and retrospection
Saihui Hou, Xinyu Pan, Chen Change Loy, Zilei Wang, and Dahua Lin · 2018
Cited alongside, same era.
Condensenet: An efficient densenet using learned group convolutions
Gao Huang, Shichen Liu, Laurens Van der Maaten, and Kilian Q Weinberger · 2018
Cited alongside, same era.
Quantization and training of neural networks for efficient integer-arithmetic-only inference
Benoit Jacob, Skirmantas Kligys, Bo Chen, Menglong Zhu, Matthew Tang, Andrew Howard, Hartwig Adam, and Dmitry Kalenichenko · 2018
Cited alongside, same era.
FearNet: Brain-inspired model for incremental learning
Ronald Kemker and Christopher Kanan · 2018
Cited alongside, same era.
Mingxing Tan and Quoc Le · 2019
Later among the works it cites.
Mnasnet: Platform-aware neural architecture search for mobile
Mingxing Tan, Bo Chen, Ruoming Pang, Vijay Vasudevan, Mark Sandler, Andrew Howard, and Quoc V Le · 2019
Later among the works it cites.
Netscore: towards universal metrics for large-scale performance analysis of deep neural networks for practical on-device edge usage
Alexander Wong · 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.
Compress: Self-supervised learning by compressing representations
Soroush Abbasi Koohpayegani, Ajinkya Tejankar, and Hamed Pirsiavash · 2020
Later among the works it cites.
Cognitively-inspired model for incremental learning using a few examples
Ali Ayub and Alan R Wagner · 2020
Later among the works it cites.
Active class incremental learning for imbalanced datasets
Eden Belouadah, Adrian Popescu, Umang Aggarwal, and Léo Saci · 2020
Later among the works it cites.
Unsupervised learning of visual features by contrasting cluster assignments
Mathilde Caron, Ishan Misra, Julien Mairal, Priya Goyal, Piotr Bojanowski, and Armand Joulin · 2020
Later among the works it cites.
Podnet: Pooled outputs distillation for small-tasks incremental learning
Arthur Douillard, Matthieu Cord, Charles Ollion, Thomas Robert, and Eduardo Valle · 2020
Later among the works it cites.
Bootstrap your own latent: A new approach to self-supervised learning
Jean-Bastien Grill, Florian Strub, Florent Altché, Corentin Tallec, Pierre H Richemond, Elena Buchatskaya, Carl Doersch, Bernardo Avila Pires, Zhaohan Daniel Guo, Mohammad Gheshlaghi Azar, et al · 2020
Later among the works it cites.
Lifelong machine learning with deep streaming linear discriminant analysis
Tyler L Hayes and Christopher Kanan · 2020
Later among the works it cites.
Remind your neural network to prevent catastrophic forgetting
Tyler L Hayes, Kushal Kafle, Robik Shrestha, Manoj Acharya, and Christopher Kanan · 2020
Later among the works it cites.
Learning to segment the tail
Xinting Hu, Yi Jiang, Kaihua Tang, Jingyuan Chen, Chunyan Miao, and Hanwang Zhang · 2020
Later among the works it cites.
Reality aware vr headsets
Joseph O’Hagan and Julie R Williamson · 2020
Later among the works it cites.
Latent replay for real-time continual learning
Lorenzo Pellegrini, Gabriele Graffieti, Vincenzo Lomonaco, and Davide Maltoni · 2020
Later among the works it cites.
Openloris-object: A robotic vision dataset and benchmark for lifelong deep learning
Qi She, Fan Feng, Xinyue Hao, Qihan Yang, Chuanlin Lan, Vincenzo Lomonaco, Xuesong Shi, Zhengwei Wang, Yao Guo, Yimin Zhang, et al · 2020
Later among the works it cites.
A comparative study of calibration methods for imbalanced class incremental learning
Umang Aggarwal, Adrian Popescu, Eden Belouadah, and Celine Hudelot · 2021
Later among the works it cites.
F-siol-310: A robotic dataset and benchmark for few-shot incremental object learning
Ali Ayub and Alan R Wagner · 2021
Later among the works it cites.
Continual learning on the edge with tensorflow lite
Giorgos Demosthenous and Vassilis Vassiliades · 2021
Later among the works it cites.
Seed: Self-supervised distillation for visual representation
Zhiyuan Fang, Jianfeng Wang, Lijuan Wang, Lei Zhang, Yezhou Yang, and Zicheng Liu · 2021
Later among the works it cites.
Self-supervised training enhances online continual learning
Jhair Gallardo, Tyler L Hayes, and Christopher Kanan · 2021
Later among the works it cites.
Replay in deep learning: Current approaches and missing biological elements
Tyler L Hayes, Giri P Krishnan, Maxim Bazhenov, Hava T Siegelmann, Terrence J Sejnowski, and Christopher Kanan · 2021
Later among the works it cites.
Continual learning at the edge: Real-time training on smartphone devices
Lorenzo Pellegrini, Vincenzo Lomonaco, Gabriele Graffieti, and Davide Maltoni · 2021
Later among the works it cites.