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Most approaches in few-shot learning rely on costly annotated data related to the goal task domain during (pre-)training.
One-Shot Learning of Object Categories
Li Fei-Fei, Rob Fergus, and Pietro Perona · 2006
Earlier work this paper cites.
Visualizing data using t-sne
Laurens van der Maaten and Geoffrey Hinton · 2008
Earlier work this paper cites.
Caltech-UCSD Birds 200
P. Welinder, S. Branson, T. Mita, C. Wah, F. Schroff, S. Belongie, and P. Perona · 2010
Earlier work this paper cites.
One-shot Learning of Simple Visual Concepts
Brenden Lake, Ruslan Salakhutdinov, Jason Gross, and Joshua Tenenbaum · 2011
Earlier work this paper cites.
The Caltech-UCSD Birds-200-2011 Dataset
Catherine Wah, Steve Branson, Peter Welinder, Pietro Perona, and Serge Belongie · 2011
Earlier work this paper cites.
Imagenet Classification With Deep Convolutional Neural Networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton · 2012
Earlier work this paper cites.
Adam: A Method for Stochastic Optimization
Diederik P. Kingma and Jimmy Lei Ba · 2015
Earlier work this paper cites.
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
Earlier work this paper cites.
Using Deep Learning for Image-Based Plant Disease Detection
Sharada P Mohanty, David P Hughes, and Marcel Salathé · 2016
Earlier work this paper cites.
One-shot learning with memory-augmented neural networks
Adam Santoro, Sergey Bartunov, Matthew Botvinick, Daan Wierstra, and Timothy P. Lillicrap · 2016
Earlier work this paper cites.
Matching Networks for One Shot Learning
Oriol Vinyals, Charles Blundell, Timothy Lillicrap, and Daan Wierstra · 2016
Earlier work this paper cites.
Improved Regularization of Convolutional Neural Networks With Cutout
Terrance DeVries and Graham W Taylor · 2017
Earlier work this paper cites.
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
Chelsea Finn, Pieter Abbeel, and Sergey Levine · 2017
Earlier work this paper cites.
Optimization as a Model for Few-Shot Learning
Sachin Ravi and Hugo Larochelle · 2017
Earlier work this paper cites.
Prototypical Networks for Few-Shot Learning
Jake Snell, Kevin Swersky, and Richard Zemel · 2017
Cited alongside, same era.
Chestx-ray8: Hospital-Scale Chest X-Ray Database and Benchmarks on Weakly-Supervised Classification and Localization of Common Thorax Diseases
Xiaosong Wang, Yifan Peng, Le Lu, Zhiyong Lu, Mohammadhadi Bagheri, and Ronald M Summers · 2017
Cited alongside, same era.
Few-shot learning with metric-agnostic conditional embeddings
Nathan Hilliard, Lawrence Phillips, Scott Howland, Artëm Yankov, Courtney D. Corley, and Nathan O. Hodas · 2018
Cited alongside, same era.
Realistic Evaluation of Deep Semi-Supervised Learning Algorithms
Avital Oliver, Augustus Odena, Colin A Raffel, Ekin Dogus Cubuk, and Ian Goodfellow · 2018
Cited alongside, same era.
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
Cited alongside, same era.
Momentum Contrast for Unsupervised Visual Representation Learning
Kaiming He, Haoqi Fan, Yuxin Wu, Saining Xie, and Ross Girshick · 2019
Later among the works it cites.
Eurosat: A Novel Dataset and Deep Learning Benchmark for Land Use and Land Cover Classification
Patrick Helber, Benjamin Bischke, Andreas Dengel, and Damian Borth · 2019
Later among the works it cites.
Unsupervised Learning via Meta-Learning
Kyle Hsu, Sergey Levine, and Chelsea Finn · 2019
Later among the works it cites.
Unsupervised Few-shot Learning via Self-supervised Training
Zilong Ji, Xiaolong Zou, Tiejun Huang, and Si Wu · 2019
Later among the works it cites.
Unsupervised Meta-Learning for Few-Shot Image Classification
Siavash Khodadadeh, Ladislau Boloni, and Mubarak Shah · 2019
Later among the works it cites.
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The HAM10000 Dataset, a Large Collection of Multi-Source Dermatoscopic Images of Common Pigmented Skin Lesions
Philipp Tschandl, Cliff Rosendahl, and Harald Kittler · 2018
Cited alongside, same era.
Antreas Antoniou and Amos Storkey · 2019
Cited alongside, same era.
Self-Supervised Learning For Few-Shot Image Classification
Da Chen, Yuefeng Chen, Yuhong Li, Feng Mao, Yuan He, and Hui Xue · 2019
Cited alongside, same era.
A Closer Look at Few-shot Classification
Wei-Yu Chen, Yen-Cheng Liu, Zsolt Kira, Yu-Chiang Frank Wang, and Jia-Bin Huang · 2019
Cited alongside, same era.
Noel Codella, Veronica Rotemberg, Philipp Tschandl, M Emre Celebi, Stephen Dusza, David Gutman, Brian Helba, Aadi Kalloo, Konstantinos Liopyris, Michael Marchetti, et al · 2019
Cited alongside, same era.
Autoaugment: Learning augmentation strategies from data
Ekin D. Cubuk, Barret Zoph, Dandelion Mane, Vijay Vasudevan, and Quoc V. Le · 2019
Cited alongside, same era.
Subspace Networks for Few-Shot Classification
Arnout Devos and Matthias Grossglauser · 2019
Cited alongside, same era.
Self-Supervised Generalisation with Meta-Auxiliary Learning
Shikun Liu, Andrew Davison, and Edward Johns · 2019
Later among the works it cites.
When Does Self-Supervision Improve Few-Shot Learning?
Jong-Chyi Su, Subhransu Maji, and Bharath Hariharan · 2019
Later among the works it cites.
Unsupervised Embedding Learning via Invariant and Spreading Instance Feature
Mang Ye, Xu Zhang, Pong C Yuen, and Shih-Fu Chang · 2019
Later among the works it cites.
A Simple Framework for Contrastive Learning of Visual Representations
Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton · 2020
Closest in time.
Prototypical Contrastive Learning of Unsupervised Representations
Junnan Li, Pan Zhou, Caiming Xiong, Richard Socher, and Steven CH Hoi · 2020
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Unsupervised Few-shot Learning via Distribution Shift-based Augmentation
Tiexin Qin, Wenbin Li, Yinghuan Shi, and Yang Gao · 2020
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What makes for good views for contrastive learning
Yonglong Tian, Chen Sun, Ben Poole, Dilip Krishnan, Cordelia Schmid, and Phillip Isola · 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
Closest in time.
Random Erasing Data Augmentation
Zhun Zhong, Liang Zheng, Guoliang Kang, Shaozi Li, and Yi Yang · 2020
Closest in time.