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Real-world data is often unbalanced and long-tailed, but deep models struggle to recognize rare classes in the presence of frequent classes.
Elements of information theory
T. Cover and J. Thomas · 1991
Earlier work this paper cites.
Smote: synthetic minority over-sampling technique
Nitesh V Chawla, Kevin W Bowyer, Lawrence O Hall, and W Philip Kegelmeyer · 2002
Earlier work this paper cites.
C 4 . 5 , class imbalance , and cost sensitivity : Why under-sampling beats oversampling
Chris Drummond · 2003
Earlier work this paper cites.
Euclidean embedding of co-occurrence data
Amir Globerson, Gal Chechik, Fernando CN Pereira, and Naftali Tishby · 2004
Earlier work this paper cites.
Neighbourhood components analysis
Jacob Goldberger, Geoffrey E Hinton, Sam Roweis, and Russ R Salakhutdinov · 2004
Earlier work this paper cites.
Borderline-smote: A new over-sampling method in imbalanced data sets learning
H. Han, W. Wang, and B. Mao · 2005
Earlier work this paper cites.
Distance metric learning for large margin nearest neighbor classification
Kilian Q Weinberger, John Blitzer, and Lawrence K Saul · 2006
Earlier work this paper cites.
Visualizing data using (t-sne)
Laurens van der Maaten and Geoffrey Hinton · 2008
Earlier work this paper cites.
Imagenet: A large-scale hierarchical image database
J. Deng, W. Dong, R. Socher, L. Li, K. Li, and F. Li · 2009
Earlier work this paper cites.
Learning from imbalanced data
H. He and E. A. Garcia · 2009
Earlier work this paper cites.
Learning multiple layers of features from tiny images
Alex Krizhevsky · 2009
Earlier work this paper cites.
Distributionally robust optimization and its tractable approximations
Joel Goh and Melvyn Sim · 2010
Earlier work this paper cites.
Metric learning: A survey
Brian Kulis et al · 2012
Earlier work this paper cites.
A distributional interpretation of robust optimization
Huan Xu, Constantine Caramanis, and Shie Mannor · 2012
Earlier work this paper cites.
The nature of statistical learning theory
Vladimir Vapnik · 2013
Earlier work this paper cites.
Robust classification under sample selection bias
Anqi Liu and Brian D. Ziebart · 2014
Earlier work this paper cites.
Distributionally robust logistic regression
Soroosh Shafieezadeh-Abadeh, Peyman Mohajerin Esfahani, and D. Kuhn · 2015
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Factors in finetuning deep model for object detection with long-tail distribution
Wanli Ouyang, X. Wang, Cong Zhang, and X. Yang · 2016
Cited alongside, same era.
The inaturalist challenge 2017 dataset
G. Horn, O. Aodha, Y. Song, A. Shepard, H. Adam, P. Perona, and S. Belongie · 2017
Cited alongside, same era.
Focal loss for dense object detection
T. Lin, P. Goyal, R. Girshick, K. He, and P. Dollár · 2017
Cited alongside, same era.
Prototypical networks for few-shot learning
Jake Snell, Kevin Swersky, and Richard S Zemel · 2017
Cited alongside, same era.
On the robustness of semantic segmentation models to adversarial attacks
Anurag Arnab, Ondrej Miksik, and Philip HS Torr · 2018
Cited alongside, same era.
Large-scale long-tailed recognition in an open world
Ziwei Liu, Zhongqi Miao, Xiaohang Zhan, Jiayun Wang, Boqing Gong, and Stella X Yu · 2019
Later among the works it cites.
Distributionally robust optimization: A review
H. Rahimian and S. Mehrotra · 2019
Later among the works it cites.
Anchor loss: Modulating loss scale based on prediction difficulty
S. Ryou, S. Jeong, and P. Perona · 2019
Later among the works it cites.
Dynamic curriculum learning for imbalanced data classification
Yiru Wang, Weihao Gan, Jie Yang, Wei Wu, and Junjie Yan · 2019
Later among the works it cites.
Feature space augmentation for long-tailed data
Peng Chu, Xiao Bian, Shaopeng Liu, and Haibin Ling · 2020
Later among the works it cites.
Learning to segment the tail
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Long-tailed recognition by routing diverse distribution-aware experts
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Learning from multiple experts: Self-paced knowledge distillation for long-tailed classification
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