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The Self-Optimal-Transport (SOT) feature transform is designed to upgrade the set of features of a data instance to facilitate downstream matching or grouping related tasks.
The hungarian method for the assignment problem
Harold W Kuhn · 1955
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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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Learning multiple layers of features from tiny images
Alex Krizhevsky and Geoffrey Hinton · 2009
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The caltech-ucsd birds-200-2011 dataset
Catherine Wah, Steve Branson, Peter Welinder, Pietro Perona, and Serge J. Belongie · 2011
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Sinkhorn distances: Lightspeed computation of optimal transport
Marco Cuturi · 2013
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
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Deepreid: Deep filter pairing neural network for person re-identification
Wei Li, Rui Zhao, Tong Xiao, and Xiaogang Wang · 2014
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Coherency sensitive hashing
Simon Korman and Shai Avidan · 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, Alexander C. Berg, and Li Fei-Fei · 2015
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Scalable person re-identification: A benchmark
Liang Zheng, Liyue Shen, Lu Tian, Shengjin Wang, Jingdong Wang, and Qi Tian · 2015
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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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Deep adaptive image clustering
Jianlong Chang, Lingfeng Wang, Gaofeng Meng, Shiming Xiang, and Chunhong Pan · 2017
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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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Pointnet: Deep learning on point sets for 3d classification and segmentation
Charles R Qi, Hao Su, Kaichun Mo, and Leonidas J Guibas · 2017
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Optimization as a model for few-shot learning
Sachin Ravi and Hugo Larochelle · 2017
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A simple neural network module for relational reasoning
Adam Santoro, David Raposo, David G Barrett, Mateusz Malinowski, Razvan Pascanu, Peter Battaglia, and Timothy Lillicrap · 2017
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Prototypical networks for few-shot learning
Jake Snell, Kevin Swersky, and Richard Zemel · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
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Deep sets
Manzil Zaheer, Satwik Kottur, Siamak Ravanbakhsh, Barnabas Poczos, Russ R Salakhutdinov, and Alexander J Smola · 2017
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Re-ranking person re-identification with k-reciprocal encoding
Zhun Zhong, Liang Zheng, Donglin Cao, and Shaozi Li · 2017
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Deep clustering for unsupervised learning of visual features
Mathilde Caron, Piotr Bojanowski, Armand Joulin, and Matthijs Douze · 2018
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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 · 2018
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Leveraging the feature distribution in transfer-based few-shot learning
Yuqing Hu, Vincent Gripon, and Stéphane Pateux · 2020
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Charting the right manifold: Manifold mixup for few-shot learning
Puneet Mangla, Nupur Kumari, Abhishek Sinha, Mayank Singh, Balaji Krishnamurthy, and Vineeth N Balasubramanian · 2020
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On learning sets of symmetric elements
Haggai Maron, Or Litany, Gal Chechik, and Ethan Fetaya · 2020
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Top-db-net: Top dropblock for activation enhancement in person re-identification
Rodolfo Quispe and Helio Pedrini · 2020
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Superglue: Learning feature matching with graph neural networks
Paul-Edouard Sarlin, Daniel DeTone, Tomasz Malisiewicz, and Andrew Rabinovich · 2020
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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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Meta-learning with differentiable closed-form solvers
Luca Bertinetto, Joao F. Henriques, Philip Torr, and Andrea Vedaldi · 2019
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Are few-shot learning benchmarks too simple? solving them without task supervision at test-time
Gabriel Huang, Hugo Larochelle, and Simon Lacoste-Julien · 2019
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Invariant information clustering for unsupervised image classification and segmentation
Xu Ji, Joao F Henriques, and Andrea Vedaldi · 2019
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Style transfer by relaxed optimal transport and self-similarity
Nicholas Kolkin, Jason Salavon, and Gregory Shakhnarovich · 2019
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Set transformer: A framework for attention-based permutation-invariant neural networks
Juho Lee, Yoonho Lee, Jungtaek Kim, Adam Kosiorek, Seungjin Choi, and Yee Whye Teh · 2019
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Wouter Van Gansbeke, Simon Vandenhende, Stamatios Georgoulis, Marc Proesmans, and Luc Van Gool · 2020
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Differentiable top-k with optimal transport
Yujia Xie, Hanjun Dai, Minshuo Chen, Bo Dai, Tuo Zhao, Hongyuan Zha, Wei Wei, and Tomas Pfister · 2020
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Few-shot learning via embedding adaptation with set-to-set functions
Han-Jia Ye, Hexiang Hu, De-Chuan Zhan, and Fei Sha · 2020
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Deepemd: Few-shot image classification with differentiable earth mover’s distance and structured classifiers
Chi Zhang, Yujun Cai, Guosheng Lin, and Chunhua Shen · 2020
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Laplacian regularized few-shot learning
Imtiaz Masud Ziko, Jose Dolz, Eric Granger, and Ismail Ben Ayed · 2020
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Few-shot learning by integrating spatial and frequency representation
Xiangyu Chen and Guanghui Wang · 2021
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Relational embedding for few-shot classification
Dahyun Kang, Heeseung Kwon, Juhong Min, and Minsu Cho · 2021
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Transformers in vision: A survey
Salman Khan, Muzammal Naseer, Munawar Hayat, Syed Waqas Zamir, Fahad Shahbaz Khan, and Mubarak Shah · 2021
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A trainable optimal transport embedding for feature aggregation and its relationship to attention
Grégoire Mialon, Dexiong Chen, Alexandre d’Aspremont, and Julien Mairal · 2021
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Deep learning for person re-identification: A survey and outlook
Mang Ye, Jianbing Shen, Gaojie Lin, Tao Xiang, Ling Shao, and Steven CH Hoi · 2021
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Sill-net: Feature augmentation with separated illumination representation
Haipeng Zhang, Zhong Cao, Ziang Yan, and Changshui Zhang · 2021
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