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We introduce Transductive Infomation Maximization (TIM) for few-shot learning.
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Chelsea Finn, Pieter Abbeel, and Sergey Levine · 2017
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Weihua Hu, Takeru Miyato, Seiya Tokui, Eiichi Matsumoto, and Masashi Sugiyama · 2017
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Jake Snell, Kevin Swersky, and Richard Zemel · 2017
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N. Mishra, M. Rohaninejad, X. Chen, and P. A. Abbeel · 2018
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Alex Nichol, Joshua Achiam, and John Schulman · 2018
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Tadam: Task dependent adaptive metric for improved few-shot learning
Boris Oreshkin, Pau Rodríguez López, and Alexandre Lacoste · 2018
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Meta-learning for semi-supervised few-shot classification
Mengye Ren, Eleni Triantafillou, Sachin Ravi, Jake Snell, Kevin Swersky, Joshua B Tenenbaum, Hugo Larochelle, and Richard S Zemel · 2018
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Meta-transfer learning for few-shot learning
Qianru Sun, Yaoyao Liu, Tat-Seng Chua, and Bernt Schiele · 2019
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Representation learning with contrastive predictive coding
Aaron van den Oord, Yazhe Li, and Oriol Vinyals · 2019
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Simpleshot: Revisiting nearest-neighbor classification for few-shot learning
Yan Wang, Wei-Lun Chao, Kilian Q Weinberger, and Laurens van der Maaten · 2019
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Few-shot learning with localization in realistic settings
Davis Wertheimer and Bharath Hariharan · 2019
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Variational few-shot learning
Jian Zhang, Chenglong Zhao, Bingbing Ni, Minghao Xu, and Xiaokang Yang · 2019
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Flood Sung, Yongxin Yang, Li Zhang, Tao Xiang, Philip HS Torr, and Timothy M Hospedales · 2018
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Mixmatch: A holistic approach to semi-supervised learning
David Berthelot, Nicholas Carlini, Ian Goodfellow, Nicolas Papernot, Avital Oliver, and Colin A Raffel · 2019
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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 · 2019
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Boosting few-shot visual learning with self-supervision
Spyros Gidaris, Andrei Bursuc, Nikos Komodakis, Patrick Pérez, and Matthieu Cord · 2019
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Learning deep representations by mutual information estimation and maximization
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Cross attention network for few-shot classification
Ruibing Hou, Hong Chang, MA Bingpeng, Shiguang Shan, and Xilin Chen · 2019
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Edge-labeling graph neural network for few-shot learning
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A unifying mutual information view of metric learning: cross-entropy vs. pairwise losses
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A baseline for few-shot image classification
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Attentive weights generation for few shot learning via information maximization
Yiluan Guo and Ngai-Man Cheung · 2020
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Empirical bayes transductive meta-learning with synthetic gradients
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Prototype rectification for few-shot learning
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Rethinking few-shot image classification: a good embedding is all you need?
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Cross-domain few-shot classification via learned feature-wise transformation
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Learning embedding adaptation for few-shot learning
Han-Jia Ye, Hexiang Hu, De-Chuan Zhan, and Fei Sha · 2020
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Laplacian regularized few-shot learning
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