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Long-tailed learning has attracted much attention recently, with the goal of improving generalisation for tail classes.
Neighbourhood components analysis
Jacob Goldberger, Sam T. Roweis, Geoffrey E. Hinton, and Ruslan Salakhutdinov · 2004
Earlier work this paper cites.
A study of gaussian mixture models of color and texture features for image classification and segmentation
Haim H. Permuter, Joseph M. Francos, and Ian Jermyn · 2006
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Exploiting privileged information from web data for image categorization
Wen Li, Li Niu, and Dong Xu · 2014
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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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Classification with noisy labels by importance reweighting
Tongliang Liu and Dacheng Tao · 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 to model the tail
Yu-Xiong Wang, Deva Ramanan, and Martial Hebert · 2017
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Webvision database: Visual learning and understanding from web data
Wen Li, Limin Wang, Wei Li, Eirikur Agustsson, and Luc Van Gool · 2017
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Temporal ensembling for semi-supervised learning
Samuli Laine and Timo Aila · 2017
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The inaturalist species classification and detection dataset
Grant Van Horn, Oisin Mac Aodha, Yang Song, Yin Cui, Chen Sun, Alexander Shepard, Hartwig Adam, Pietro Perona, and Serge J. Belongie · 2018
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Dimensionality-driven learning with noisy labels
Xingjun Ma, Yisen Wang, Michael E. Houle, Shuo Zhou, Sarah M. Erfani, Shu-Tao Xia, Sudanthi Wijewickrema, and James Bailey · 2018
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Mentornet: Learning data-driven curriculum for very deep neural networks on corrupted labels
Lu Jiang, Zhengyuan Zhou, Thomas Leung, Li-Jia Li, and Li Fei-Fei · 2018
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Co-teaching: Robust training of deep neural networks with extremely noisy labels
Bo Han, Quanming Yao, Xingrui Yu, Gang Niu, Miao Xu, Weihua Hu, Ivor W. Tsang, and Masashi Sugiyama · 2018
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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
Earlier work this paper cites.
LVIS: A dataset for large vocabulary instance segmentation
Agrim Gupta, Piotr Dollár, and Ross B. Girshick · 2019
Cited alongside, same era.
Learning imbalanced datasets with label-distribution-aware margin loss
Kaidi Cao, Colin Wei, Adrien Gaidon, Nikos Aréchiga, and Tengyu Ma · 2019
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
Cited alongside, same era.
L_DMI: An information-theoretic noise-robust loss function
Yilun Xu, Peng Cao, Yuqing Kong, and Yizhou Wang · 2019
Cited alongside, same era.
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
Cited alongside, same era.
Mixmatch: A holistic approach to semi-supervised learning
David Berthelot, Nicholas Carlini, Ian Goodfellow, Nicolas Papernot, Avital Oliver, and Colin Raffel · 2019
Identifying mislabeled data using the area under the margin ranking
Geoff Pleiss, Tianyi Zhang, Ethan R. Elenberg, and Kilian Q. Weinberger · 2020
Later among the works it cites.
BBN: bilateral-branch network with cumulative learning for long-tailed visual recognition
Boyan Zhou, Quan Cui, Xiu-Shen Wei, and Zhao-Min Chen · 2020
Later among the works it cites.
Long-tailed classification by keeping the good and removing the bad momentum causal effect
Kaihua Tang, Jianqiang Huang, and Hanwang Zhang · 2020
Later among the works it cites.
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
Later among the works it cites.
Balanced meta-softmax for long-tailed visual recognition
Jiawei Ren, Cunjun Yu, Shunan Sheng, Xiao Ma, Haiyu Zhao, Shuai Yi, and Hongsheng Li · 2020
Later among the works it cites.
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Cited alongside, same era.
Understanding and utilizing deep neural networks trained with noisy labels
Pengfei Chen, Ben Ben Liao, Guangyong Chen, and Shengyu Zhang · 2019
Cited alongside, same era.
Equalization loss for long-tailed object recognition
Jingru Tan, Changbao Wang, Buyu Li, Quanquan Li, Wanli Ouyang, Changqing Yin, and Junjie Yan · 2020
Cited alongside, same era.
Does tail label help for large-scale multi-label learning?
Tong Wei and Yu-Feng Li · 2020
Cited alongside, same era.
Decoupling representation and classifier for long-tailed recognition
Bingyi Kang, Saining Xie, Marcus Rohrbach, Zhicheng Yan, Albert Gordo, Jiashi Feng, and Yannis Kalantidis · 2020
Cited alongside, same era.
Distribution-balanced loss for multi-label classification in long-tailed datasets
Tong Wu, Qingqiu Huang, Ziwei Liu, Yu Wang, and Dahua Lin · 2020
Cited alongside, same era.
Rethinking the value of labels for improving class-imbalanced learning
Yuzhe Yang and Zhi Xu · 2020
Cited alongside, same era.
SELF: learning to filter noisy labels with self-ensembling
Duc Tam Nguyen, Chaithanya Kumar Mummadi, Thi - Phuong - Nhung Ngo, Thi Hoai Phuong Nguyen, Laura Beggel, and Thomas Brox · 2020
Later among the works it cites.
Adversarial robustness under long-tailed distribution
Tong Wu, Ziwei Liu, Qingqiu Huang, Yu Wang, and Dahua Lin · 2021
Closest in time.
Long-tailed recognition by routing diverse distribution-aware experts
Xudong Wang, Long Lian, Zhongqi Miao, Ziwei Liu, and Stella Yu · 2021
Closest in time.
Long-tail learning via logit adjustment
Aditya Krishna Menon, Sadeep Jayasumana, Ankit Singh Rawat, Himanshu Jain, Andreas Veit, and Sanjiv Kumar · 2021
Closest in time.
Mopro: Webly supervised learning with momentum prototypes
Junnan Li, Caiming Xiong, and Steven CH Hoi · 2021
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Robust early-learning: Hindering the memorization of noisy labels
Xiaobo Xia, Tongliang Liu, Bo Han, Chen Gong, Nannan Wang, Zongyuan Ge, and Yi Chang · 2021
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Heteroskedastic and imbalanced deep learning with adaptive regularization
Kaidi Cao, Yining Chen, Junwei Lu, Nikos Arechiga, Adrien Gaidon, and Tengyu Ma · 2021
Closest in time.
Distributional robustness loss for long-tail learning
Dvir Samuel and Gal Chechik · 2021
Closest in time.
Improving calibration for long-tailed recognition
Zhisheng Zhong, Jiequan Cui, Shu Liu, and Jiaya Jia · 2021
Closest in time.