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In the context of few-shot learning, it is currently believed that a fixed pre-trained (PT) model, along with fine-tuning the final layer during evaluation, outperforms standard meta-learning algorithms.
Meta-dataset: A dataset of datasets for learning to learn from few examples, 2020
Eleni Triantafillou, Tyler Zhu, Vincent Dumoulin, Pascal Lamblin, Utku Evci, Kelvin Xu, Ross Goroshin, Carles Gelada, Kevin Swersky, Pierre-Antoine Manzagol, and Hugo Larochelle · 1903
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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 · 1904
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A baseline for few-shot image classification, 2020
Guneet S. Dhillon, Pratik Chaudhari, Avinash Ravichandran, and Stefano Soatto · 1909
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Rapid learning or feature reuse? towards understanding the effectiveness of maml, 2020
Aniruddh Raghu, Maithra Raghu, Samy Bengio, and Oriol Vinyals · 1909
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Shaoli Huang and Dacheng Tao · 1911
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A broader study of cross-domain few-shot learning, 2020
Yunhui Guo, Noel C. Codella, Leonid Karlinsky, James V. Codella, John R. Smith, Kate Saenko, Tajana Rosing, and Rogerio Feris · 1912
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Big transfer (bit): General visual representation learning
Alexander Kolesnikov, Lucas Beyer, Xiaohua Zhai, Joan Puigcerver, Jessica Yung, Sylvain Gelly, and Neil Houlsby · 1912
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A power primer
Jacob Cohen · 1992
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Uncovering the connections between adversarial transferability and knowledge transferability
Kaizhao Liang, Jacky Y. Zhang, Boxin Wang, Zhuolin Yang, Oluwasanmi Koyejo, and Bo Li · 2006
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A visual vocabulary for flower classification
M.-E. Nilsback and A. Zisserman · 2006
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The Advantage of Conditional Meta-Learning for Biased Regularization and Fine-Tuning
Giulia Denevi, Massimiliano Pontil, and Carlo Ciliberto · 2008
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Generative Language Modeling for Automated Theorem Proving
Stanislas Polu and Ilya Sutskever · 2009
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Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E. Hinton · 2012
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Describing textures in the wild, 2013
Mircea Cimpoi, Subhransu Maji, Iasonas Kokkinos, Sammy Mohamed, and Andrea Vedaldi · 2013
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Too big to fail: Large samples and the p-value problem
Mingfeng Lin, Henry Lucas, and Galit Shmueli · 2013
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Fine-grained visual classification of aircraft, 2013
Subhransu Maji, Esa Rahtu, Juho Kannala, Matthew Blaschko, and Andrea Vedaldi · 2013
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Playing Atari with Deep Reinforcement Learning
Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Alex Graves, Ioannis Antonoglou, Daan Wierstra, and Martin Riedmiller · 2013
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Human-level concept learning through probabilistic program induction
Brenden M. Lake, Ruslan Salakhutdinov, and Joshua B. Tenenbaum · 2015
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Learning to learn by gradient descent by gradient descent, 2016
Marcin Andrychowicz, Misha Denil, Sergio Gomez, Matthew W. Hoffman, David Pfau, Tom Schaul, Brendan Shillingford, and Nando de Freitas · 2016
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Deep Residual Learning for Image Recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Mastering the game of Go with deep neural networks and tree search
David Silver, Aja Huang, Chris J. Maddison, Arthur Guez, Laurent Sifre, George Van Den Driessche, Julian Schrittwieser, Ioannis Antonoglou, Veda Panneershelvam, Marc Lanctot, Sander Dieleman, Dominik Grewe, John Nham, Nal Kalchbrenner, Ilya Sutskever, Timothy Lillicrap, Madeleine Leach, Koray Kavukcuoglu, Thore Graepel, and Demis Hassabis · 2016
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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A New Meta-Baseline for Few-Shot Learning
Yinbo Chen, Xiaolong Wang, Zhuang Liu, Huijuan Xu, and Trevor Darrell · 2020
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Mathematical Reasoning via Self-supervised Skip-tree Training
Markus N Rabe, Google Research, Dennis Lee, Kshitij Bansal, and Christian Szegedy · 2020
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Embedding propagation: Smoother manifold for few-shot classification, 2020
Pau Rodríguez, Issam Laradji, Alexandre Drouin, and Alexandre Lacoste · 2020
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Rethinking Few-Shot Image Classification: a Good Embedding Is All You Need?, 2020
Yonglong Tian, Yue Wang, Dilip Krishnan, Joshua B. Tenenbaum, and Phillip Isola · 2020
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Model-agnostic meta-learning for fast adaptation of deep networks, 2017
Chelsea Finn, Pieter Abbeel, and Sergey Levine · 2017
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Adam: A method for stochastic optimization, 2017
Diederik P. Kingma and Jimmy Ba · 2017
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Matching networks for one shot learning, 2017
Oriol Vinyals, Charles Blundell, Timothy Lillicrap, Koray Kavukcuoglu, and Daan Wierstra · 2017
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BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
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Adafactor: Adaptive learning rates with sublinear memory cost, 2018
Noam Shazeer and Mitchell Stern · 2018
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Meta-learning with differentiable closed-form solvers, 2019
Luca Bertinetto, João F. Henriques, Philip H. S. Torr, and Andrea Vedaldi · 2019
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Memory efficient meta-learning with large images
J. Bronskill, D. Massiceti, M. Patacchiola, K. Hofmann, S. Nowozin, and R. E. Turner · 2021
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Universal representation learning from multiple domains for few-shot classification, 2021
Wei-Hong Li, Xialei Liu, and Hakan Bilen · 2021
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An Online Learning Approach to Interpolation and Extrapolation in Domain Generalization
Elan Rosenfeld, Pradeep Ravikumar, and Andrej Risteski · 2021
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The Role of Global Labels in Few-Shot Classification and How to Infer Them
Ruohan Wang, Massimiliano Pontil, and Carlo Ciliberto · 2021
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Mastering Atari Games with Limited Data
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Delaunay: A dataset of abstract art for psychophysical and machine learning research
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Proof artifact co-training for theorem proving with language models, 2022
Jesse Michael Han, Jason Rute, Yuhuai Wu, Edward W. Ayers, and Stanislas Polu · 2022
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The Effect of Diversity in Meta-Learning
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Brando Miranda, Patrick Yu, Yu-Xiong Wang, and Sanmi Koyejo · 2022
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Transformer models for type inference in the simply typed lambda calculus: A case study in deep learning for code
B. Miranda, A. Shinnar, V. Pestun, and B. Trager · 2023
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