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Long short-term memory
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Face recognition from a single image per person: A survey
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Tracking multiple occluding people by localizing on multiple scene planes
Saad M Khan and Mubarak Shah · 2008
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Visualizing data using t-sne
Laurens Van der Maaten and Geoffrey Hinton · 2008
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Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
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Learning multiple layers of features from tiny images
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The Caltech-UCSD Birds-200-2011 Dataset
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The stability-plasticity dilemma: Investigating the continuum from catastrophic forgetting to age-limited learning effects
Martial Mermillod, Aurélia Bugaiska, and Patrick Bonin · 2013
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Accurate scale estimation for robust visual tracking
Martin Danelljan, Gustav Häger, Fahad Khan, and Michael Felsberg · 2014
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An empirical investigation of catastrophic forgeting in gradientbased neural networks
Ian J Goodfellow, Mehdi Mirza, Aaron Courville Da Xiao, and Yoshua Bengio · 2014
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Dropout: a simple way to prevent neural networks from overfitting
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Siamese neural networks for one-shot image recognition
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Distilling the knowledge in a neural network
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2015
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Learnware: on the future of machine learning
Zhi-Hua Zhou · 2016
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Matching networks for one shot learning
Oriol Vinyals, Charles Blundell, Timothy Lillicrap, Daan Wierstra, et al · 2016
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Jimmy Lei Ba, Jamie Ryan Kiros, and Geoffrey E Hinton · 2016
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A survey on ensemble learning for data stream classification
Heitor Murilo Gomes, Jean Paul Barddal, Fabrício Enembreck, and Albert Bifet · 2017
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Learning without forgetting
Zhizhong Li and Derek Hoiem · 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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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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Model-agnostic meta-learning for fast adaptation of deep networks
Chelsea Finn, Pieter Abbeel, and Sergey Levine · 2017
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Continual learning through synaptic intelligence
Friedemann Zenke, Ben Poole, and Surya Ganguli · 2017
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Gradient episodic memory for continual learning
David Lopez-Paz and Marc’Aurelio Ranzato · 2017
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Optimization as a model for few-shot learning
Sachin Ravi and Hugo Larochelle · 2017
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Prototypical networks for few-shot learning
Jake Snell, Kevin Swersky, and Richard Zemel · 2017
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Few-shot object recognition from machine-labeled web images
Zhongwen Xu, Linchao Zhu, and Yi Yang · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
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A structured self-attentive sentence embedding
Zhouhan Lin, Minwei Feng, Cicero Nogueira dos Santos, Mo Yu, Bing Xiang, Bowen Zhou, and Yoshua Bengio · 2017
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Deep sets
Manzil Zaheer, Satwik Kottur, Siamak Ravanbakhsh, Barnabas Poczos, Russ R Salakhutdinov, and Alexander J Smola · 2017
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Memory aware synapses: Learning what (not) to forget
Rahaf Aljundi, Francesca Babiloni, Mohamed Elhoseiny, Marcus Rohrbach, and Tinne Tuytelaars · 2018
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Reinforced continual learning
Ju Xu and Zhanxing Zhu · 2018
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Lifelong learning with dynamically expandable networks
Jaehong Yoon, Eunho Yang, Jeongtae Lee, and Sung Ju Hwang · 2018
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Efficient lifelong learning with a-gem
Arslan Chaudhry, Marc’Aurelio Ranzato, Marcus Rohrbach, and Mohamed Elhoseiny · 2018
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A closer look at few-shot classification
Wei-Yu Chen, Yen-Cheng Liu, Zsolt Kira, Yu-Chiang Frank Wang, and Jia-Bin Huang · 2018
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On first-order meta-learning algorithms
Alex Nichol, Joshua Achiam, and John Schulman · 2018
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Learning to adapt: Meta-learning for model-based control
Ignasi Clavera, Anusha Nagabandi, Ronald S Fearing, Pieter Abbeel, Sergey Levine, and Chelsea Finn · 2018
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Meta-learning for low-resource neural machine translation
Jiatao Gu, Yong Wang, Yun Chen, Kyunghyun Cho, and Victor OK Li · 2018
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3fabrec: Fast few-shot face alignment by reconstruction
Bjorn Browatzki and Christian Wallraven · 2020
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Xtarnet: Learning to extract task-adaptive representation for incremental few-shot learning
Sung Whan Yoon, Do-Yeon Kim, Jun Seo, and Jaekyun Moon · 2020
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Language models are few-shot learners
Tom B. Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel M. Ziegler, Jeffrey Wu, Clemens Winter, Christopher Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever, and Dario Amodei · 2020
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Condition aware and revise transformer for question answering
Xinyan Zhao, Feng Xiao, Haoming Zhong, Jun Yao, and Huanhuan Chen · 2020
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Generative pretraining from pixels
Mark Chen, Alec Radford, Rewon Child, Jeffrey Wu, Heewoo Jun, David Luan, and Ilya Sutskever · 2020
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Chelsea Finn, Kelvin Xu, and Sergey Levine · 2018
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How to train your maml
Antreas Antoniou, Harrison Edwards, and Amos Storkey · 2018
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The effects of negative adaptation in model-agnostic meta-learning
Tristan Deleu and Yoshua Bengio · 2018
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Gradient-based meta-learning with learned layerwise metric and subspace
Yoonho Lee and Seungjin Choi · 2018
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Learning to compare: Relation network for few-shot learning
Flood Sung, Yongxin Yang, Li Zhang, Tao Xiang, Philip HS Torr, and Timothy M Hospedales · 2018
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Low-shot learning with imprinted weights
Hang Qi, Matthew Brown, and David G Lowe · 2018
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Dynamic few-shot visual learning without forgetting
Spyros Gidaris and Nikos Komodakis · 2018
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A survey on visual transformer
Kai Han, Yunhe Wang, Hanting Chen, Xinghao Chen, Jianyuan Guo, Zhenhua Liu, Yehui Tang, An Xiao, Chunjing Xu, Yixing Xu, et al · 2020
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An image is worth 16x16 words: Transformers for image recognition at scale
Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, et al · 2020
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End-to-end object detection with transformers
Nicolas Carion, Francisco Massa, Gabriel Synnaeve, Nicolas Usunier, Alexander Kirillov, and Sergey Zagoruyko · 2020
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Polytransform: Deep polygon transformer for instance segmentation
Justin Liang, Namdar Homayounfar, Wei-Chiu Ma, Yuwen Xiong, Rui Hu, and Raquel Urtasun · 2020
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Incremental object detection via meta-learning
KJ Joseph, Jathushan Rajasegaran, Salman Khan, Fahad Shahbaz Khan, and Vineeth Balasubramanian · 2021
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Transformers in vision: A survey
Salman Khan, Muzammal Naseer, Munawar Hayat, Syed Waqas Zamir, Fahad Shahbaz Khan, and Mubarak Shah · 2021
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Learning placeholders for open-set recognition
Da-Wei Zhou, Han-Jia Ye, and De-Chuan Zhan · 2021
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Generalized out-of-distribution detection: A survey
Jingkang Yang, Kaiyang Zhou, Yixuan Li, and Ziwei Liu · 2021
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Semantically coherent out-of-distribution detection
Jingkang Yang, Haoqi Wang, Litong Feng, Xiaopeng Yan, Huabin Zheng, Wayne Zhang, and Ziwei Liu · 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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Self-promoted prototype refinement for few-shot class-incremental learning
Kai Zhu, Yang Cao, Wei Zhai, Jie Cheng, and Zheng-Jun Zha · 2021
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A continual learning survey: Defying forgetting in classification tasks
Matthias Delange, Rahaf Aljundi, Marc Masana, Sarah Parisot, Xu Jia, Ales Leonardis, Greg Slabaugh, and Tinne Tuytelaars · 2021
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Pycil: A python toolbox for class-incremental learning
Da-Wei Zhou, Fu-Yun Wang, Han-Jia Ye, and De-Chuan Zhan · 2021
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Der: Dynamically expandable representation for class incremental learning
Shipeng Yan, Jiangwei Xie, and Xuming He · 2021
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How to train your maml to excel in few-shot classification
Han-Jia Ye and Wei-Lun Chao · 2021
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When maml can adapt fast and how to assist when it cannot
Sébastien Arnold, Shariq Iqbal, and Fei Sha · 2021
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Hierarchical graph attention network for few-shot visual-semantic learning
Chengxiang Yin, Kun Wu, Zhengping Che, Bo Jiang, Zhiyuan Xu, and Jian Tang · 2021
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Mgsvf: Multi-grained slow vs. fast framework for few-shot class-incremental learning
Hanbin Zhao, Yongjian Fu, Mintong Kang, Qi Tian, Fei Wu, and Xi Li · 2021
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Few-shot class-incremental learning via relation knowledge distillation
Songlin Dong, Xiaopeng Hong, Xiaoyu Tao, Xinyuan Chang, Xing Wei, and Yihong Gong · 2021
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Few-shot lifelong learning
Pratik Mazumder, Pravendra Singh, and Piyush Rai · 2021
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Semantic-aware knowledge distillation for few-shot class-incremental learning
Ali Cheraghian, Shafin Rahman, Pengfei Fang, Soumava Kumar Roy, Lars Petersson, and Mehrtash Harandi · 2021
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Learning adaptive classifiers synthesis for generalized few-shot learning
Han-Jia Ye, Hexiang Hu, and De-Chuan Zhan · 2021
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Subspace regularizers for few-shot class incremental learning
Afra Feyza Akyürek, Ekin Akyürek, Derry Wijaya, and Jacob Andreas · 2021
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Pyramid vision transformer: A versatile backbone for dense prediction without convolutions
Wenhai Wang, Enze Xie, Xiang Li, Deng-Ping Fan, Kaitao Song, Ding Liang, Tong Lu, Ping Luo, and Ling Shao · 2021
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Trar: Routing the attention spans in transformer for visual question answering
Yiyi Zhou, Tianhe Ren, Chaoyang Zhu, Xiaoshuai Sun, Jianzhuang Liu, Xinghao Ding, Mingliang Xu, and Rongrong Ji · 2021
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Foster: Feature boosting and compression for class-incremental learning
Fu-Yun Wang, Da-Wei Zhou, Han-Jia Ye, and De-Chuan Zhan · 2022
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A model or 603 exemplars: Towards memory-efficient class-incremental learning
Da-Wei Zhou, Qi-Wei Wang, Han-Jia Ye, and De-Chuan Zhan · 2022
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Few-shot learning with a strong teacher
Han-Jia Ye, Lu Ming, De-Chuan Zhan, and Wei-Lun Chao · 2022
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Forward compatible few-shot class-incremental learning
Da-Wei Zhou, Fu-Yun Wang, Han-Jia Ye, Liang Ma, Shiliang Pu, and De-Chuan Zhan · 2022
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