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Existing long-tailed recognition methods, aiming to train class-balanced models from long-tailed data, generally assume the models would be evaluated on the uniform test class distribution.
Smote: synthetic minority over-sampling technique
Nitesh V Chawla, Kevin W Bowyer, et al · 2002
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Semi-supervised learning by entropy minimization
Yves Grandvalet, Yoshua Bengio, et al · 2005
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Transfer joint matching for unsupervised domain adaptation
Mingsheng Long, Jianmin Wang, Guiguang Ding, Jiaguang Sun, and Philip S Yu · 2014
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How transferable are features in deep neural networks?
Jason Yosinski, Jeff Clune, Yoshua Bengio, and Hod Lipson · 2014
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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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A discriminative feature learning approach for deep face recognition
Yandong Wen, Kaipeng Zhang, et al · 2016
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Detecting and correcting for label shift with black box predictors
Zachary Lipton, Yu-Xiang Wang, and Alexander Smola · 2018
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The inaturalist species classification and detection dataset
Grant Van Horn, Oisinand Mac Aodha, et al · 2018
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Online adaptive asymmetric active learning for budgeted imbalanced data
Yifan Zhang, Peilin Zhao, Jiezhang Cao, Wenye Ma, Junzhou Huang, Qingyao Wu, and Mingkui Tan · 2018
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Adaptive cost-sensitive online classification
Peilin Zhao, Yifan Zhang, Min Wu, Steven CH Hoi, Mingkui Tan, and Junzhou Huang · 2018
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Learning imbalanced datasets with label-distribution-aware margin loss
Kaidi Cao, Colin Wei, Adrien Gaidon, Nikos Arechiga, and Tengyu Ma · 2019
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Class-balanced loss based on effective number of samples
Yin Cui, Menglin Jia, et al · 2019
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Survey on deep learning with class imbalance
Justin M Johnson and Taghi M Khoshgoftaar · 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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A unifying mutual information view of metric learning: cross-entropy vs. pairwise losses
Malik Boudiaf, Jérôme Rony, et al · 2020
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Improved baselines with momentum contrastive learning
Xinlei Chen, Haoqi Fan, Ross Girshick, and Kaiming He · 2020
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Randaugment: Practical automated data augmentation with a reduced search space
Ekin Dogus Cubuk, Barret Zoph, Jon Shlens, and Quoc Le · 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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Balanced meta-softmax for long-tailed visual recognition
Ren Jiawei, Cunjun Yu, Xiao Ma, Haiyu Zhao, Shuai Yi, et al · 2020
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Targeted data-driven regularization for out-of-distribution generalization
Mohammad Mahdi Kamani, Sadegh Farhang, Mehrdad Mahdavi, and James Z Wang · 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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Learning loss for test-time augmentation
Ildoo Kim, Younghoon Kim, and Sungwoong Kim · 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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Long-tailed recognition using class-balanced experts
Saurabh Sharma, Ning Yu, Mario Fritz, and Bernt Schiele · 2020
Cited alongside, same era.
Test-time training with self-supervision for generalization under distribution shifts
Yu Sun, Xiaolong Wang, Zhuang Liu, John Miller, Alexei Efros, and Moritz Hardt · 2020
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Equalization loss for long-tailed object recognition
Test-time classifier adjustment module for model-agnostic domain generalization
Yusuke Iwasawa and Yutaka Matsuo · 2021
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Exploring balanced feature spaces for representation learning
Bingyi Kang, Yu Li, Sa Xie, Zehuan Yuan, and Jiashi Feng · 2021
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Ttt++: When does self-supervised test-time training fail or thrive?
Yuejiang Liu, Parth Kothari, Bastien van Delft, Baptiste Bellot-Gurlet, Taylor Mordan, and Alexandre Alahi · 2021
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Long-tail learning via logit adjustment
Aditya Krishna Menon, Sadeep Jayasumana, Ankit Singh Rawat, Himanshu Jain, Andreas Veit, and Sanjiv Kumar · 2021
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Generalization on unseen domains via inference-time label-preserving target projections
Prashant Pandey, Mrigank Raman, Sumanth Varambally, and Prathosh AP · 2021
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Influence-balanced loss for imbalanced visual classification
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Jingru Tan, Changbao Wang, Buyu Li, Quanquan Li, Wanli Ouyang, Changqing Yin, and Junjie Yan · 2020
Cited alongside, same era.
Long-tailed classification by keeping the good and removing the bad momentum causal effect
Kaihua Tang, Jianqiang Huang, and Hanwang Zhang · 2020
Cited alongside, same era.
Posterior re-calibration for imbalanced datasets
Junjiao Tian, Yen-Cheng Liu, et al · 2020
Cited alongside, same era.
Test-time unsupervised domain adaptation
Thomas Varsavsky, Mauricio Orbes-Arteaga, et al · 2020
Cited alongside, same era.
Batchensemble: an alternative approach to efficient ensemble and lifelong learning
Yeming Wen, Dustin Tran, and Jimmy Ba · 2020
Cited alongside, same era.
Learning from multiple experts: Self-paced knowledge distillation for long-tailed classification
Liuyu Xiang, Guiguang Ding, and Jungong Han · 2020
Cited alongside, same era.
Collaborative unsupervised domain adaptation for medical image diagnosis
Yifan Zhang, Ying Wei, et al · 2020
Cited alongside, same era.
Seulki Park, Jongin Lim, Younghan Jeon, and Jin Young Choi · 2021
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Source-free domain adaptation via avatar prototype generation and adaptation
Zhen Qiu, Yifan Zhang, Hongbin Lin, Shuaicheng Niu, Yanxia Liu, Qing Du, and Mingkui Tan · 2021
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Tent: Fully test-time adaptation by entropy minimization
Dequan Wang, Evan Shelhamer, Shaoteng Liu, Bruno Olshausen, and Trevor Darrell · 2021
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Seesaw loss for long-tailed instance segmentation
Jiaqi Wang, Wenwei Zhang, Yuhang Zang, Yuhang Cao, Jiangmiao Pang, Tao Gong, Kai Chen, Ziwei Liu, Chen Change Loy, and Dahua Lin · 2021
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Contrastive learning based hybrid networks for long-tailed image classification
Peng Wang, Kai Han, et al · 2021
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Long-tailed recognition by routing diverse distribution-aware experts
Xudong Wang, Long Lian, Zhongqi Miao, Ziwei Liu, and Stella X Yu · 2021
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Unsupervised discovery of the long-tail in instance segmentation using hierarchical self-supervision
Zhenzhen Weng, Mehmet Giray Ogut, Shai Limonchik, and Serena Yeung · 2021
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Fasa: Feature augmentation and sampling adaptation for long-tailed instance segmentation
Yuhang Zang, Chen Huang, and Chen Change Loy · 2021
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Distribution alignment: A unified framework for long-tail visual recognition
Songyang Zhang, Zeming Li, Shipeng Yan, Xuming He, and Jian Sun · 2021
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Unleashing the power of contrastive self-supervised visual models via contrast-regularized fine-tuning
Yifan Zhang, Bryan Hooi, Lanqing Hong, and Jiashi Feng · 2021
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Deep long-tailed learning: A survey
Yifan Zhang, Bingyi Kang, Bryan Hooi, Shuicheng Yan, and Jiashi Feng · 2021
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Improving calibration for long-tailed recognition
Zhisheng Zhong, Jiequan Cui, Shu Liu, and Jiaya Jia · 2021
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Relieving long-tailed instance segmentation via pairwise class balance
Yin-Yin He, Peizhen Zhang, Xiu-Shen Wei, Xiangyu Zhang, and Jian Sun · 2022
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Prototype-guided continual adaptation for class-incremental unsupervised domain adaptation
Hongbin Lin, Yifan Zhang, Zhen Qiu, Shuaicheng Niu, Chuang Gan, Yanxia Liu, and Mingkui Tan · 2022
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Efficient test-time model adaptation without forgetting
Shuaicheng Niu, Jiaxiang Wu, Yifan Zhang, Yaofo Chen, Shijian Zheng, Peilin Zhao, and Mingkui Tan · 2022
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Optimal transport for long-tailed recognition with learnable cost matrix
Hanyu Peng, Mingming Sun, and Ping Li · 2022
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