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We develop a transductive meta-learning method that uses unlabelled instances to improve few-shot image classification performance.
Clustering with bregman divergences
Arindam Banerjee, Srujana Merugu, Inderjit S Dhillon, and Joydeep Ghosh · 2005
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Automated flower classification over a large number of classes
Maria-Elena Nilsback and Andrew Zisserman · 2008
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Learning multiple layers of features from tiny images
Alex Krizhevsky · 2009
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MNIST handwritten digit database
Yann LeCun and Corinna Cortes · 2010
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The caltech-ucsd birds-200-2011 dataset
Catherine Wah, Steve Branson, Peter Welinder, Pietro Perona, and Serge Belongie · 2011
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Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey Hinton · 2012
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A survey on metric learning for feature vectors and structured data
Aurélien Bellet, Amaury Habrard, and Marc Sebban · 2013
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Detection of traffic signs in real-world images: The german traffic sign detection benchmark
Sebastian Houben, Johannes Stallkamp, Jan Salmen, Marc Schlipsing, and Christian Igel · 2013
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Fine-grained visual classification of aircraft
Subhransu Maji, Esa Rahtu, Juho Kannala, Matthew Blaschko, and Andrea Vedaldi · 2013
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Describing textures in the wild
Mircea Cimpoi, Subhransu Maji, Iasonas Kokkinos, Sammy Mohamed, and Andrea Vedaldi · 2014
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Generative adversarial nets
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2014
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Microsoft coco: Common objects in context
Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollár, and C Lawrence Zitnick · 2014
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On k-means algorithm with the use of mahalanobis distances
Igor Melnykov and Volodymyr Melnykov · 2014
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Very deep convolutional networks for large-scale image recognition
Karen Simonyan and Andrew Zisserman · 2014
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Going deeper with convolutions
Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott E. Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, and Andrew Rabinovich · 2014
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How transferable are features in deep neural networks?
Jason Yosinski, Jeff Clune, Yoshua Bengio, and Hod Lipson · 2014
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Siamese neural networks for one-shot image recognition
Gregory Koch, Richard Zemel, and Ruslan Salakhutdinov · 2015
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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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Faster R-CNN: Towards real-time object detection with region proposal networks
Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun · 2015
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Imagenet large scale visual recognition challenge
Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, et al · 2015
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Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 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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The quick, draw!-ai experiment.(2016), 2016
Jonas Jongejan, Henry Rowley, Takashi Kawashima, Jongmin Kim, and Nick Fox-Gieg · 2016
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You only look once: Unified, real-time object detection
J. Redmon, S. Divvala, R. Girshick, and A. Farhadi · 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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Model-agnostic meta-learning for fast adaptation of deep networks
Chelsea Finn, Pieter Abbeel, and Sergey Levine · 2017
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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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A comprehensive survey of deep learning for image captioning
MD. Zakir Hossain, Ferdous Sohel, Mohd Fairuz Shiratuddin, and Hamid Laga · 2019
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A survey of deep learning-based object detection
Licheng Jiao, Fan Zhang, Fang Liu, Shuyuan Yang, Lingling Li, Zhixi Feng, and Rong Qu · 2019
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Edge-labeling graph neural network for few-shot learning
Jongmin Kim, Taesup Kim, Sungwoong Kim, and Chang D. Yoo · 2019
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Learning to propagate labels: Transductive propagation network for few-shot learning
Yanbin Liu, Juho Lee, Minseop Park, Saehoon Kim, and Yi Yang · 2019
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Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E. Hinton · 2017
Cited alongside, same era.
Meta-learning with temporal convolutions
Nikhil Mishra, Mostafa Rohaninejad, Xi Chen, and Pieter Abbeel · 2017
Cited alongside, same era.
Optimization as a model for few-shot learning
Sachin Ravi and Hugo Larochelle · 2017
Cited alongside, same era.
Prototypical networks for few-shot learning
Jake Snell, Kevin Swersky, and Richard Zemel · 2017
Cited alongside, same era.
A survey on image classification and activity recognition using deep convolutional neural network architecture
M. Sornam, K. Muthusubash, and V. Vanitha · 2017
Cited alongside, same era.
Conditional neural processes
Marta Garnelo, Dan Rosenbaum, Chris J. Maddison, Tiago Ramalho, David Saxton, Murray Shanahan, Yee Whye Teh, Danilo J. Rezende, and S. M. Ali Eslami · 2018
Cited alongside, same era.
Transductive episodic-wise adaptive metric for few-shot learning
Limeng Qiao, Yemin Shi, Jia Li, Yaowei Wang, Tiejun Huang, and Yonghong Tian · 2019
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Fast and flexible multi-task classification using conditional neural adaptive processes
James Requeima, Jonathan Gordon, John Bronskill, Sebastian Nowozin, and Richard E Turner · 2019
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A survey of zero-shot learning: Settings, methods, and applications
Wei Wang, Vincent W. Zheng, Han Yu, and Chunyan Miao · 2019
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Yaqing Wang and Quanming Yao · 2019
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Improved few-shot visual classification
Peyman Bateni, Raghav Goyal, Vaden Masrani, Frank Wood, and Leonid Sigal · 2020
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Selecting relevant features from a multi-domain representation for few-shot classification, 2020
Nikita Dvornik, Cordelia Schmid, and Julien Mairal · 2020
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Semi-supervised few-shot learning for medical image segmentation, 2020
Abdur R Feyjie, Reza Azad, Marco Pedersoli, Claude Kauffman, Ismail Ben Ayed, and Jose Dolz · 2020
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Neural RST-based evaluation of discourse coherence
Grigorii Guz, Peyman Bateni, Darius Muglich, and Giuseppe Carenini · 2020
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TAFSSL: task-adaptive feature sub-space learning for few-shot classification
Moshe Lichtenstein, Prasanna Sattigeri, Rogério Feris, Raja Giryes, and Leonid Karlinsky · 2020
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A universal representation transformer layer for few-shot image classification, 2020
Lu Liu, William Hamilton, Guodong Long, Jing Jiang, and Hugo Larochelle · 2020
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Channel attention based iterative residual learning for depth map super-resolution
Xibin Song, Yuchao Dai, Dingfu Zhou, Liu Liu, Wei Li, Hongdong Li, and Ruigang Yang · 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, Kelvin Xu, Ross Goroshin, Carles Gelada, Kevin Swersky, Pierre-Antoine Manzagol, and Hugo Larochelle · 2020
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Imagining the road ahead: Multi-agent trajectory prediction via differentiable simulation, 2021
Adam Scibior, Vasileios Lioutas, Daniele Reda, Peyman Bateni, and Frank Wood · 2021
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Remp: Rectified metric propagation for few-shot learning
Yang Zhao, Chunyuan Li, Ping Yu, and Changyou Chen · 2021
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