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Most few-shot learning techniques are pre-trained on a large, labeled "base dataset".
Visualizing data using t-sne
Laurens van der Maaten and Geoffrey Hinton · 2008
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Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
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Information theoretic measures for clusterings comparison: Variants, properties, normalization and correction for chance
Nguyen Xuan Vinh, Julien Epps, and James Bailey · 2010
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API design for machine learning software: experiences from the scikit-learn project
Lars Buitinck, Gilles Louppe, Mathieu Blondel, Fabian Pedregosa, Andreas Mueller, Olivier Grisel, Vlad Niculae, Peter Prettenhofer, Alexandre Gramfort, Jaques Grobler, Robert Layton, Jake VanderPlas, Arnaud Joly, Brian Holt, and Gaël Varoquaux · 2013
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Distilling the knowledge in a neural network
Geoffrey Hinton, Oriol Vinyals, and Jeff Dean · 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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Unsupervised learning of visual representations by solving jigsaw puzzles
Mehdi Noroozi and Paolo Favaro · 2016
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Matching networks for one shot learning
Oriol Vinyals, Charles Blundell, Timothy Lillicrap, Daan Wierstra, et al · 2016
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Colorful image colorization
Richard Zhang, Phillip Isola, and Alexei A Efros · 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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Low-shot visual recognition by shrinking and hallucinating features
Bharath Hariharan and Ross Girshick · 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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Adversarial discriminative domain adaptation
Eric Tzeng, Judy Hoffman, Kate Saenko, and Trevor Darrell · 2017
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Probabilistic model-agnostic meta-learning
Chelsea Finn, Kelvin Xu, and Sergey Levine · 2018
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Dynamic few-shot visual learning without forgetting
Spyros Gidaris and Nikos Komodakis · 2018
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Unsupervised representation learning by predicting image rotations
Spyros Gidaris, Praveer Singh, and Nikos Komodakis · 2018
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Cycada: Cycle-consistent adversarial domain adaptation
Judy Hoffman, Eric Tzeng, Taesung Park, Jun-Yan Zhu, Phillip Isola, Kate Saenko, Alexei Efros, and Trevor Darrell · 2018
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Conditional adversarial domain adaptation
Mingsheng Long, Zhangjie Cao, Jianmin Wang, and Michael I Jordan · 2018
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Domain adaptive transfer learning with specialist models
Jiquan Ngiam, Daiyi Peng, Vijay Vasudevan, Simon Kornblith, Quoc V Le, and Ruoming Pang · 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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Low-shot learning with imprinted weights
Hang Qi, Matthew Brown, and David G Lowe · 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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Learning to compare: Relation network for few-shot learning
Flood Sung, Yongxin Yang, Li Zhang, Tao Xiang, Philip HS Torr, and Timothy M Hospedales · 2018
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Deep visual domain adaptation: A survey
Mei Wang and Weihong Deng · 2018
Cited alongside, same era.
Low-shot learning from imaginary data
Yu-Xiong Wang, Ross Girshick, Martial Hebert, and Bharath Hariharan · 2018
Cited alongside, same era.
The visual task adaptation benchmark
Xiaohua Zhai, Joan Puigcerver, Alexander Kolesnikov, Pierre Ruyssen, Carlos Riquelme, Mario Lucic, Josip Djolonga, Andre Susano Pinto, Maxim Neumann, Alexey Dosovitskiy, et al · 2019
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Category anchor-guided unsupervised domain adaptation for semantic segmentation
Qiming Zhang, Jing Zhang, Wei Liu, and Dacheng Tao · 2019
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Confidence regularized self-training
Yang Zou, Zhiding Yu, Xiaofeng Liu, BVK Kumar, and Jinsong Wang · 2019
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A simple framework for contrastive learning of visual representations
Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton · 2020
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A baseline for few-shot image classification
Guneet S Dhillon, Pratik Chaudhari, Avinash Ravichandran, and Stefano Soatto · 2020
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Unsupervised feature learning via non-parametric instance discrimination
Zhirong Wu, Yuanjun Xiong, Stella X Yu, and Dahua Lin · 2018
Cited alongside, same era.
Unsupervised domain adaptation for semantic segmentation via class-balanced self-training
Yang Zou, Zhiding Yu, BVK Kumar, and Jinsong Wang · 2018
Cited alongside, same era.
Generating classification weights with gnn denoising autoencoders for few-shot learning
Spyros Gidaris and Nikos Komodakis · 2019
Cited alongside, same era.
Boosting few-shot visual learning with self-supervision
Spyros Gidaris, Andrei Bursuc, Nikos Komodakis, Patrick Pérez, and Matthieu Cord · 2019
Cited alongside, same era.
Cross attention network for few-shot classification
Ruibing Hou, Hong Chang, MA Bingpeng, Shiguang Shan, and Xilin Chen · 2019
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Meta-learning with differentiable convex optimization
Kwonjoon Lee, Subhransu Maji, Avinash Ravichandran, and Stefano Soatto · 2019
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Yunhui Guo, Noel CF Codella, Leonid Karlinsky, John R Smith, Tajana Rosing, and Rogerio Feris · 2020
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Momentum contrast for unsupervised visual representation learning
Kaiming He, Haoqi Fan, Yuxin Wu, Saining Xie, and Ross Girshick · 2020
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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 · 2020
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M-adda: Unsupervised domain adaptation with deep metric learning
Issam H Laradji and Reza Babanezhad · 2020
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Instance adaptive self-training for unsupervised domain adaptation
Ke Mei, Chuang Zhu, Jiaqi Zou, and Shanghang Zhang · 2020
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Self-supervised learning of pretext-invariant representations
Ishan Misra and Laurens van der Maaten · 2020
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Embedding propagation: Smoother manifold for few-shot classification
Pau Rodríguez, Issam Laradji, Alexandre Drouin, and Alexandre Lacoste · 2020
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When does self-supervision improve few-shot learning?
Jong-Chyi Su, Subhransu Maji, and Bharath Hariharan · 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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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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Extending and analyzing self-supervised learning across domains
Bram Wallace and Bharath Hariharan · 2020
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Instance credibility inference for few-shot learning
Yikai Wang, Chengming Xu, Chen Liu, Li Zhang, and Yanwei Fu · 2020
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A survey of unsupervised deep domain adaptation
Garrett Wilson and Diane J Cook · 2020
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Self-training with noisy student improves imagenet classification
Qizhe Xie, Minh-Thang Luong, Eduard Hovy, and Quoc V Le · 2020
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Transmatch: A transfer-learning scheme for semi-supervised few-shot learning
Zhongjie Yu, Lin Chen, Zhongwei Cheng, and Jiebo Luo · 2020
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