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Few-shot class incremental learning -- the problem of updating a trained classifier to discriminate among an expanded set of classes with limited labeled data -- is a key challenge for machine learning systems deployed in non-stationary environments.
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An embarrassingly simple approach to zero-shot learning
Bernardino Romera-Paredes and Philip Torr · 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
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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 deep representations of fine-grained visual descriptions
Scott Reed, Zeynep Akata, Honglak Lee, and Bernt Schiele · 2016
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Matching networks for one shot learning
Oriol Vinyals, Charles Blundell, Timothy Lillicrap, Koray Kavukcuoglu, and Daan Wierstra · 2016
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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 · 2016
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Sentence-bert: Sentence embeddings using siamese bert-networks
Nils Reimers and Iryna Gurevych · 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
David Rolnick, Arun Ahuja, Jonathan Schwarz, Timothy Lillicrap, and Gregory Wayne · 2019
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Generalized zero-shot learning via aligned variational autoencoders
Edgar Schönfeld, Sayna Ebrahimi, Samarth Sinha, Trevor Darrell, and Zeynep Akata · 2019
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Baby steps towards few-shot learning with multiple semantics
Eli Schwartz, Leonid Karlinsky, Rogerio Feris, Raja Giryes, and Alex M Bronstein · 2019
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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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A simple neural attentive meta-learner
Nikhil Mishra, Mostafa Rohaninejad, Xi Chen, and Pieter Abbeel · 2017
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icarl: Incremental classifier and representation learning
S Rebuffi, Alexander Kolesnikov, and Christoph H Lampert · 2017
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Prototypical networks for few-shot learning
Jake Snell, Kevin Swersky, and Richard Zemel · 2017
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Dynamic few-shot visual learning without forgetting
Spyros Gidaris and Nikos Komodakis · 2018
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Distributed weight consolidation: A brain segmentation case study
Patrick McClure, Charles Y. Zheng, J. Kaczmarzyk, John Rogers-Lee, S. Ghosh, D. Nielson, P. Bandettini, and Francisco Pereira · 2018
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Tapnet: Neural network augmented with task-adaptive projection for few-shot learning
Sung Whan Yoon, Jun Seo, and Jaekyun Moon · 2019
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Class-incremental learning: survey and performance evaluation
Marc Masana, Xialei Liu, Bartlomiej Twardowski, Mikel Menta, Andrew D Bagdanov, and Joost van de Weijer · 2020
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A review of generalized zero-shot learning methods
Farhad Pourpanah, Moloud Abdar, Yuxuan Luo, Xinlei Zhou, Ran Wang, Chee Peng Lim, and Xi-Zhao Wang · 2020
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Adaptive subspaces for few-shot learning
Christian Simon, Piotr Koniusz, Richard Nock, and Mehrtash Harandi · 2020
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Rethinking few-shot image classification: a good embedding is all you need?
Yonglong Tian, Yue Wang, Dilip Krishnan, Joshua B Tenenbaum, and Phillip Isola · 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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On the dangers of stochastic parrots: Can language models be too big?
Emily M Bender, Timnit Gebru, Angelina McMillan-Major, and Shmargaret Shmitchell · 2021
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Incremental few-shot learning via vector quantization in deep embedded space
Kuilin Chen and Chi-Guhn Lee · 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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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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Learning transferable visual models from natural language supervision
Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al · 2021
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Aligning visual prototypes with bert embeddings for few-shot learning
Kun Yan, Zied Bouraoui, Ping Wang, Shoaib Jameel, and Steven Schockaert · 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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