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Neural networks trained with class-imbalanced data are known to perform poorly on minor classes of scarce training data.
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Nitesh V Chawla, Kevin W Bowyer, Lawrence O Hall, and W Philip Kegelmeyer · 2002
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Tiny imagenet visual recognition challenge
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Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, and Andrew Rabinovich · 2015
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Algorithms efficiency measurement on imbalanced data using geometric mean and cross validation
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Ms-celeb-1m: A dataset and benchmark for large-scale face recognition
Yandong Guo, Lei Zhang, Yuxiao Hu, Xiaodong He, and Jianfeng Gao · 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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Learning deep representation for imbalanced classification
Chen Huang, Yining Li, Chen Change Loy, and Xiaoou Tang · 2016
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Solving the under-fitting problem for decision tree algorithms by incremental swarm optimization in rare-event healthcare classification
Jinyan Li, Simon Fong, Sabah Mohammed, Jinan Fiaidhi, Qian Chen, and Zhen Tan · 2016
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Factors in finetuning deep model for object detection with long-tail distribution
Wanli Ouyang, Xiaogang Wang, Cong Zhang, and Xiaokang Yang · 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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Yfcc100m: The new data in multimedia research
Bart Thomee, David A Shamma, Gerald Friedland, Benjamin Elizalde, Karl Ni, Douglas Poland, Damian Borth, and Li-Jia Li · 2016
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Training deep neural networks on imbalanced data sets
Shoujin Wang, Wei Liu, Jia Wu, Longbing Cao, Qinxue Meng, and Paul J Kennedy · 2016
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Active bias: Training more accurate neural networks by emphasizing high variance samples
Haw-Shiuan Chang, Erik Learned-Miller, and Andrew McCallum · 2017
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Class rectification hard mining for imbalanced deep learning
Qi Dong, Shaogang Gong, and Xiatian Zhu · 2017
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One-shot face recognition by promoting underrepresented classes
Yandong Guo and Lei Zhang · 2017
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Low-shot visual recognition by shrinking and hallucinating features
Bharath Hariharan and Ross Girshick · 2017
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Discriminative sparse neighbor approximation for imbalanced learning
Chen Huang, Chen Change Loy, and Xiaoou Tang · 2017
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Densely connected convolutional networks
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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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Visual genome: Connecting language and vision using crowdsourced dense image annotations
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Focal loss for dense object detection
Tsung-Yi Lin, Priya Goyal, Ross Girshick, Kaiming He, and Piotr Dollár · 2017
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The devil is in the tails: Fine-grained classification in the wild
Grant Van Horn and Pietro Perona · 2017
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Learning to model the tail
Yu-Xiong Wang, Deva Ramanan, and Martial Hebert · 2017
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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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Shiori Sagawa, Pang Wei Koh, Tatsunori B Hashimoto, and Percy Liang · 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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Manifold mixup: Better representations by interpolating hidden states
Vikas Verma, Alex Lamb, Christopher Beckham, Amir Najafi, Ioannis Mitliagkas, David Lopez-Paz, and Yoshua Bengio · 2019
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Wgan-based synthetic minority over-sampling technique: Improving semantic fine-grained classification for lung nodules in ct images
Qingfeng Wang, Xuehai Zhou, Chao Wang, Zhiqin Liu, Jun Huang, Ying Zhou, Changlong Li, Hang Zhuang, and Jie-Zhi Cheng · 2019
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Yue Wu, Hongfu Liu, Jun Li, and Yun Fu · 2017
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Aggregated residual transformations for deep neural networks
Saining Xie, Ross B. Girshick, Piotr Dollár, Zhuowen Tu, and Kaiming He · 2017
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Range loss for deep face recognition with long-tailed training data
Xiao Zhang, Zhiyuan Fang, Yandong Wen, Zhifeng Li, and Yu Qiao · 2017
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A systematic study of the class imbalance problem in convolutional neural networks
Mateusz Buda, Atsuto Maki, and Maciej A Mazurowski · 2018
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Large scale fine-grained categorization and domain-specific transfer learning
Yin Cui, Yang Song, Chen Sun, Andrew Howard, and Serge Belongie · 2018
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Imbalanced deep learning by minority class incremental rectification
Qi Dong, Shaogang Gong, and Xiatian Zhu · 2018
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Smote for learning from imbalanced data: progress and challenges, marking the 15-year anniversary
Alberto Fernández, Salvador Garcia, Francisco Herrera, and Nitesh V Chawla · 2018
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Simpleshot: Revisiting nearest-neighbor classification for few-shot learning
Yan Wang, Wei-Lun Chao, Kilian Q Weinberger, and Laurens van der Maaten · 2019
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Dynamic curriculum learning for imbalanced data classification
Yiru Wang, Weihao Gan, Jie Yang, Wei Wu, and Junjie Yan · 2019
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Feature transfer learning for face recognition with under-represented data
Xi Yin, Xiang Yu, Kihyuk Sohn, Xiaoming Liu, and Manmohan Chandraker · 2019
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A study on action detection in the wild
Yubo Zhang, Pavel Tokmakov, Martial Hebert, and Cordelia Schmid · 2019
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Unequal-training for deep face recognition with long-tailed noisy data
Yaoyao Zhong, Weihong Deng, Mei Wang, Jiani Hu, Jianteng Peng, Xunqiang Tao, and Yaohai Huang · 2019
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Remix: Rebalanced mixup
Hsin-Ping Chou, Shih-Chieh Chang, Jia-Yu Pan, Wei Wei, and Da-Cheng Juan · 2020
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Deep imbalanced learning for face recognition and attribute prediction
Chen Huang, Yining Li, Change Loy Chen, and Xiaoou Tang · 2020
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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
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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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Adjusting decision boundary for class imbalanced learning
Byungju Kim and Junmo Kim · 2020
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M2m: Imbalanced classification via major-to-minor translation
Jaehyung Kim, Jongheon Jeong, and Jinwoo Shin · 2020
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Overcoming classifier imbalance for long-tail object detection with balanced group softmax
Yu Li, Tao Wang, Bingyi Kang, Sheng Tang, Chunfeng Wang, Jintao Li, and Jiashi Feng · 2020
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Balanced meta-softmax for long-tailed visual recognition
Jiawei Ren, Cunjun Yu, Shunan Sheng, Xiao Ma, Haiyu Zhao, Shuai Yi, and Hongsheng Li · 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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Long-tailed classification by keeping the good and removing the bad momentum causal effect
Kaihua Tang, Jianqiang Huang, and Hanwang Zhang · 2020
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Frustratingly simple few-shot object detection
Xin Wang, Thomas E Huang, Trevor Darrell, Joseph E Gonzalez, and Fisher Yu · 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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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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BBN: bilateral-branch network with cumulative learning for long-tailed visual recognition
Boyan Zhou, Quan Cui, Xiu-Shen Wei, and Zhao-Min Chen · 2020
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Long-tailed recognition by routing diverse distribution-aware experts
Xudong Wang, Long Lian, Zhongqi Miao, et al · 2021
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