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Model-agnostic meta-learning (MAML) is a popular method for few-shot learning but assumes that we have access to the meta-training set.
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Explaining and harnessing adversarial examples
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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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Model-agnostic meta-learning for fast adaptation of deep networks
Chelsea Finn, Pieter Abbeel, and Sergey Levine · 2017
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Simple and scalable predictive uncertainty estimation using deep ensembles
Balaji Lakshminarayanan, Alexander Pritzel, and Charles Blundell · 2017
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Few-shot adversarial domain adaptation
Saeid Motiian, Quinn Jones, Seyed Iranmanesh, and Gianfranco Doretto · 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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Flipout: Efficient pseudo-independent weight perturbations on mini-batches
Yeming Wen, Paul Vicol, Jimmy Ba, Dustin Tran, and Roger Grosse · 2018
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Chengxiang Yin, Jian Tang, Zhiyuan Xu, and Yanzhi Wang · 2018
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The importance of better models in stochastic optimization
Hilal Asi and John C. Duchi · 2019
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Domain-symmetric networks for adversarial domain adaptation
Yabin Zhang, Hui Tang, Kui Jia, and Mingkui Tan · 2019
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Bridging the gap between constant step size stochastic gradient descent and markov chains
Aymeric Dieuleveut, Alain Durmus, Francis Bach, et al · 2020
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Uncertainty-guided continual learning with bayesian neural networks
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Adversarial discriminative domain adaptation
Eric Tzeng, Judy Hoffman, Kate Saenko, and Trevor Darrell · 2017
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Recasting gradient-based meta-learning as hierarchical bayes
Erin Grant, Chelsea Finn, Sergey Levine, Trevor Darrell, and Thomas Griffiths · 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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Towards deep learning models resistant to adversarial attacks
Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, and Adrian Vladu · 2018
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A simple neural attentive meta-learner
Nikhil Mishra, Mostafa Rohaninejad, Xi Chen, and Pieter Abbeel · 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
Cited alongside, same era.
Sayna Ebrahimi, Mohamed Elhoseiny, Trevor Darrell, and Marcus Rohrbach · 2020
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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 · 2020
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Cross-domain few-shot classification via learned feature-wise transformation
Hung-Yu Tseng, Hsin-Ying Lee, Jia-Bin Huang, and Ming-Hsuan Yang · 2020
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A survey of unsupervised deep domain adaptation
Garrett Wilson and Diane J. Cook · 2020
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Adversarial examples improve image recognition
Cihang Xie, Mingxing Tan, Boqing Gong, Jiang Wang, Alan L. Yuille, and Quoc V. Le · 2020
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Domain-adaptive few-shot learning
An Zhao, Mingyu Ding, Zhiwu Lu, Tao Xiang, Yulei Niu, Jiechao Guan, Ji Rong Wen, and Ping Luo · 2020
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