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Recently, it has been observed that a transfer learning solution might be all we need to solve many few-shot learning benchmarks -- thus raising important questions about when and how meta-learning algorithms should be deployed.
Meta-Dataset: A Dataset of Datasets for Learning to Learn from Few Examples
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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Torchmeta: A Meta-Learning library for PyTorch, 2019
Tristan Deleu, Tobias Würfl, Mandana Samiei, Joseph Paul Cohen, and Yoshua Bengio · 1909
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A Baseline for Few-Shot Image Classification
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
Aniruddh Raghu, Maithra Raghu, and Samy Bengio · 1909
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On the Measure of Intelligence
François Chollet · 1911
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Shaoli Huang and Dacheng Tao · 1911
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A Broader Study of Cross-Domain Few-Shot Learning
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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The Need for Biases in Learning Generalizations by The Need for Biases in Learning Generalizations
Tom M Mitchell · 1980
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Vision: A Computational Investigation into the Human Representation and Processing of Visual Information
David Marr · 1982
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No Free Lunch Theorems for Optimization
David H Wolpert and William G Macready · 1997
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Unraveling Meta-Learning: Understanding Feature Representations for Few-Shot Tasks
Micah Goldblum, Steven Reich, Liam Fowl, Renkun Ni, Valeriia Cherepanova, and Tom Goldstein · 2002
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"learn2learn: A library for Meta-Learning research"
Sébastien M R "Arnold, Praateek Mahajan, Debajyoti Datta, Ian Bunner, and Konstantinos Saitas" Zarkias · 2008
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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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Modeling and Optimization Trade-off in Meta-learning
Katelyn Gao and Ozan Sener · 2010
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ImageNet Classification with Deep Convolutional Neural Networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton · 2012
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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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Understanding Machine Learning: From Theory to Algorithms
Shai Ben-David Shai Shalev-Shwartz · 2014
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Deep Residual Learning for Image Recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Building Machines That Learn and Think Like People
Brenden M. Lake, Tomer D. Ullman, Joshua B. Tenenbaum, and Samuel J. Gershman · 2016
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The Information Complexity of Learning Tasks, their Structure and their Distance
Alessandro Achille, Giovanni Paolini, Glen Mbeng, and Stefano Soatto · 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 Openai · 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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LEVELS OF ANALYSIS FOR MACHINE LEARNING
Jessica Hamrick and Shakir Mohamed · 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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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
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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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SVCCA: Singular Vector Canonical Correlation Analysis for Deep Learning Dynamics and Interpretability
Maithra Raghu, Justin Gilmer, Jason Yosinski, and Jascha Sohl-Dickstein · 2017
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Optimization as a model for few-shot learning
Sachin Ravi and Hugo Larochelle · 2017
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Alessandro Achille, Glen Mbeng, and Stefano Soatto · 2018
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BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Jacob Devlin, Ming Wei Chang, Kenton Lee, and Kristina Toutanova · 2018
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Insights on representational similarity in neural networks with canonical correlation
Ari S. Morcos, Maithra Raghu, and Samy Bengio · 2018
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Generalization Bounds For Meta-Learning: An Information-Theoretic Analysis
Qi Chen, Changjian Shui, and Mario Marchand · 2021
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Grounding Representation Similarity with Statistical Testing
Frances Ding, Jean-Stanislas Denain, and Jacob Steinhardt · 2021
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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 · 2021
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Does MAML Only Work via Feature Re-use? A Data Centric Perspective
Brando Miranda, Yu-Xiong Wang, and Sanmi Koyejo · 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, Gretchen Krueger, and Ilya Sutskever · 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
Weirui Ye, Shaohuai Liu, Thanard Kurutach, Pieter Abbeel, Yang Gao, Tsinghua University, U C Berkeley, Shanghai Qi, and Zhi Institute · 2021
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The Effect of Diversity in Meta-Learning
Ramnath Kumar, Tristan Deleu, and Yoshua Bengio · 2022
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