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Deep classifiers have achieved great success in visual recognition.
Addressing the curse of imbalanced training sets: One-sided selection
Miroslav Kubat and Stan Matwin · 1997
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Smote: Synthetic minority over-sampling technique
Nitesh V. Chawla, Kevin W. Bowyer, Lawrence O. Hall, and W. Philip Kegelmeyer · 2002
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Borderline-smote: a new over-sampling method in imbalanced data sets learning
Hui Han, Wen-Yuan Wang, and Bing-Huan Mao · 2005
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Restricted decontamination for the imbalanced training sample problem
Ricardo Barandela, E Rangel, Jose Salvador Sanchez, and Francesc J Ferri · 2009
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ImageNet: A Large-Scale Hierarchical Image Database
J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei · 2009
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Learning from imbalanced data
H. He and E. A. Garcia · 2009
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On the complexity of linear prediction: Risk bounds, margin bounds, and regularization
Sham M Kakade, Karthik Sridharan, and Ambuj Tewari · 2009
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Learning multiple layers of features from tiny images
Alex Krizhevsky · 2012
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Distributed representations of words and phrases and their compositionality
Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg S Corrado, and Jeff Dean · 2013
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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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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2015
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Learning deep representation for imbalanced classification
Chen Huang, Yining Li, Chen Change Loy, and Xiaoou Tang · 2016
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Relay backpropagation for effective learning of deep convolutional neural networks
Li Shen, Zhouchen Lin, and Qingming Huang · 2016
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Deep metric learning via lifted structured feature embedding
Hyun Oh Song, Yu Xiang, Stefanie Jegelka, and Silvio Savarese · 2016
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Categorical reparametrization with gumbel-softmax
Eric Jang, Shixiang Gu, and Ben Poole · 2017
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Cost-sensitive learning of deep feature representations from imbalanced data
Salman H Khan, Munawar Hayat, Mohammed Bennamoun, Ferdous A Sohel, and Roberto Togneri · 2017
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Focal loss for dense object detection
Tsung-Yi Lin, Priya Goyal, Ross B. Girshick, Kaiming He, and Piotr Dollár · 2017
Cited alongside, same era.
Sgdr: Stochastic gradient descent with warm restarts
I. Loshchilov and F. Hutter · 2017
Cited alongside, same era.
Learning to model the tail
Yu-Xiong Wang, Deva Ramanan, and Martial Hebert · 2017
Cited alongside, same era.
Class-balanced loss based on effective number of samples
Yin Cui, Menglin Jia, Tsung-Yi Lin, Yang Song, and Serge J. Belongie · 2019
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Generalized inner loop meta-learning
Edward Grefenstette, Brandon Amos, Denis Yarats, Phu Mon Htut, Artem Molchanov, Franziska Meier, Douwe Kiela, Kyunghyun Cho, and Soumith Chintala · 2019
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LVIS: A dataset for large vocabulary instance segmentation
Agrim Gupta, Piotr Dollar, and Ross Girshick · 2019
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Gaussian affinity for max-margin class imbalanced learning
Munawar Hayat, Salman Khan, Syed Waqas Zamir, Jianbing Shen, and Ling Shao · 2019
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Deep imbalanced learning for face recognition and attribute prediction
Chen Huang, Yining Li, Change Loy Chen, and Xiaoou Tang · 2019
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Range loss for deep face recognition with long-tailed training data
X. Zhang, Z. Fang, Y. Wen, Z. Li, and Y. Qiao · 2017
Cited alongside, same era.
Places: A 10 million image database for scene recognition
Bolei Zhou, Agata Lapedriza, Aditya Khosla, Aude Oliva, and Antonio Torralba · 2017
Cited alongside, same era.
A systematic study of the class imbalance problem in convolutional neural networks
Mateusz Buda, Atsuto Maki, and Maciej A Mazurowski · 2018
Cited alongside, same era.
Learning to reweight examples for robust deep learning
Mengye Ren, Wenyuan Zeng, Bin Yang, and Raquel Urtasun · 2018
Cited alongside, same era.
The implicit bias of gradient descent on separable data
Daniel Soudry, Elad Hoffer, Mor Shpigel Nacson, Suriya Gunasekar, and Nathan Srebro · 2018
Cited alongside, same era.
What is the effect of importance weighting in deep learning?
Jonathon Byrd and Zachary Lipton · 2019
Cited alongside, same era.
Buyu Li, Yu Liu, and Xiaogang Wang · 2019
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Large-scale long-tailed recognition in an open world
Ziwei Liu, Zhongqi Miao, Xiaohang Zhan, Jiayun Wang, Boqing Gong, and Stella X. Yu · 2019
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Pytorch: An imperative style, high-performance deep learning library
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Kopf, Edward Yang, Zachary DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, and Soumith Chintala · 2019
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Meta-weight-net: Learning an explicit mapping for sample weighting
Jun Shu, Qi Xie, Lixuan Yi, Qian Zhao, Sanping Zhou, Zongben Xu, and Deyu Meng · 2019
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Rethinking class-balanced methods for long-tailed visual recognition from a domain adaptation perspective
Muhammad Abdullah Jamal, Matthew Brown, Ming-Hsuan Yang, Liqiang Wang, and Boqing Gong · 2020
Closest in time.
Decoupling representation and classifier for long-tailed recognition
Bingyi Kang, Saining Xie, Marcus Rohrbach, Zhicheng Yan, Albert Gordo, Jiashi Feng, and Yannis Kalantidis · 2020
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Equalization loss for long-tailed object recognition
Jingru Tan, Changbao Wang, Buyu Li, Quanquan Li, Wanli Ouyang, Changqing Yin, and Junjie Yan · 2020
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Identifying and compensating for feature deviation in imbalanced deep learning
Han-Jia Ye, Hong-You Chen, De-Chuan Zhan, and Wei-Lun Chao · 2020
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