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We focus on the problem of learning without forgetting from multiple tasks arriving sequentially, where each task is defined using a few-shot episode of novel or already seen classes.
David Ha, Andrew Dai, and Quoc V Le · 2016
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Andrei A Rusu, Neil C Rabinowitz, Guillaume Desjardins, Hubert Soyer, James Kirkpatrick, Koray Kavukcuoglu, Razvan Pascanu, and Raia Hadsell · 2016
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Matching networks for one shot learning
Oriol Vinyals, Charles Blundell, Tim Lillicrap, Koray Kavukcuoglu, and Daan Wierstra · 2016
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Model-agnostic meta-learning for fast adaptation of deep networks
Chelsea Finn, Pieter Abbeel, and Sergey Levine · 2017
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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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Gradient episodic memory for continual learning
David Lopez-Paz and Marc’Aurelio Ranzato · 2017
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Meta networks
Tsendsuren Munkhdalai and Hong Yu · 2017
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Prototypical networks for few-shot learning
Jake Snell, Kevin Swersky, and Richard Zemel · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, and Illia Polosukhin · 2017
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Continual learning through synaptic intelligence
Friedemann Zenke, Ben Poole, and Surya Ganguli · 2017
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Dynamic few-shot visual learning without forgetting
Spyros Gidaris and Nikos Komodakis · 2018
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Re-evaluating continual learning scenarios: A categorization and case for strong baselines
Yen-Chang Hsu, Yen-Cheng Liu, Anita Ramasamy, and Zsolt Kira · 2018
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Reptile: a scalable metalearning algorithm
Alex Nichol and John Schulman · 2018
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TADAM: task dependent adaptive metric for improved few-shot learning
Boris N. Oreshkin, Pau Rodríguez López, and Alexandre Lacoste · 2018
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Learning to learn without forgetting by maximizing transfer and minimizing interference
Matthew Riemer, Ignacio Cases, Robert Ajemian, Miao Liu, Irina Rish, Yuhai Tu, and Gerald Tesauro · 2018
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Learning to compare: Relation network for few-shot learning
Flood Sung, Yongxin Yang, Li Zhang, Tao Xiang, Philip H. S. Torr, and Timothy M. Hospedales · 2018
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How to train your MAML
Antreas Antoniou, Harrison Edwards, and Amos J. Storkey · 2019
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Incremental few-shot learning with attention attractor networks
Mengye Ren, Renjie Liao, Ethan Fetaya, and Richard Zemel · 2019
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Experience replay for continual learning
Incremental few-shot object detection
Juan-Manuel Perez-Rua, Xiatian Zhu, Timothy M Hospedales, and Tao Xiang · 2020
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Adapterhub: A framework for adapting transformers
Jonas Pfeiffer, Andreas Rücklé, Clifton Poth, Aishwarya Kamath, Ivan Vulić, Sebastian Ruder, Kyunghyun Cho, and Iryna Gurevych · 2020
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Few-shot class-incremental learning
Xiaoyu Tao, Xiaopeng Hong, Xinyuan Chang, Songlin Dong, Xing Wei, and Yihong Gong · 2020
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Batchensemble: an alternative approach to efficient ensemble and lifelong learning
Yeming Wen, Dustin Tran, and Jimmy Ba · 2020
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Few-shot and continual learning with attentive independent mechanisms
Eugene Lee, Cheng-Han Huang, and Chen-Yi Lee · 2021
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David Rolnick, Arun Ahuja, Jonathan Schwarz, Timothy Lillicrap, and Gregory Wayne · 2019
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Meta-learning with latent embedding optimization
Andrei A. Rusu, Dushyant Rao, Jakub Sygnowski, Oriol Vinyals, Razvan Pascanu, Simon Osindero, and Raia Hadsell · 2019
Cited alongside, same era.
Continual learning with hypernetworks
Johannes Von Oswald, Christian Henning, João Sacramento, and Benjamin F Grewe · 2019
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Defining benchmarks for continual few-shot learning
Antreas Antoniou, Massimiliano Patacchiola, Mateusz Ochal, and Amos Storkey · 2020
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Continual lifelong learning in natural language processing: A survey
Magdalena Biesialska, Katarzyna Biesialska, and Marta R Costa-Jussa · 2020
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Incremental few-shot learning via vector quantization in deep embedded space
Kuilin Chen and Chi-Guhn Lee · 2020
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Look-ahead meta learning for continual learning
Gunshi Gupta, Karmesh Yadav, and Liam Paull · 2020
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Pratik Mazumder, Pravendra Singh, and Piyush Rai · 2021
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Continual few-shot learning for text classification
Ramakanth Pasunuru, Veselin Stoyanov, and Mohit Bansal · 2021
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Overcoming catastrophic forgetting in incremental few-shot learning by finding flat minima
Guangyuan Shi, Jiaxin Chen, Wenlong Zhang, Li-Ming Zhan, and Xiao-Ming Wu · 2021
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Few-shot continual learning for audio classification
Yu Wang, Nicholas J Bryan, Mark Cartwright, Juan Pablo Bello, and Justin Salamon · 2021
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Few-shot incremental learning with continually evolved classifiers
Chi Zhang, Nan Song, Guosheng Lin, Yun Zheng, Pan Pan, and Yinghui Xu · 2021
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Sylph: A hypernetwork framework for incremental few-shot object detection
Li Yin, Juan M Perez-Rua, and Kevin J Liang · 2022
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Hypertransformer: Model generation for supervised and semi-supervised few-shot learning
Andrey Zhmoginov, Mark Sandler, and Max Vladymyrov · 2022
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Continual few-shot learning with transformer adaptation and knowledge regularization
Xin Wang, Yue Liu, Jiapei Fan, Weigao Wen, Hui Xue, and Wenwu Zhu · 2023
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