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The current evaluation protocol of long-tailed visual recognition trains the classification model on the long-tailed source label distribution and evaluates its performance on the uniform target label distribution.
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Training cost-sensitive neural networks with methods addressing the class imbalance problem
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The influence of class imbalance on cost-sensitive learning: An empirical study
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Intelligible models for healthcare: Predicting pneumonia risk and hospital 30-day readmission
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Obtaining well calibrated probabilities using bayesian binning
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Faster r-cnn: Towards real-time object detection with region proposal networks
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Imagenet large scale visual recognition challenge
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Very deep convolutional networks for large-scale image recognition
Karen Simonyan and Andrew Zisserman · 2015
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End to end learning for self-driving cars
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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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f-gan: Training generative neural samplers using variational divergence minimization
Sebastian Nowozin, Botond Cseke, and Ryota Tomioka · 2016
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Simulation and the Monte Carlo method
Reuven Y Rubinstein and Dirk P Kroese · 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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High-resolution breast cancer screening with multi-view deep convolutional neural networks
Class-balanced loss based on effective number of samples
Yin Cui, Menglin Jia, Tsung-Yi Lin, Yang Song, and Serge Belongie · 2019
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Survey on deep learning with class imbalance
Justin M Johnson and Taghi M Khoshgoftaar · 2019
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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 · 2019
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Beyond temperature scaling: Obtaining well-calibrated multi-class probabilities with dirichlet calibration
Meelis Kull, Miquel Perello Nieto, Markus Kängsepp, Telmo Silva Filho, Hao Song, and Peter Flach · 2019
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Data-efficient mutual information neural estimator
Xiao Lin, Indranil Sur, Samuel A Nastase, Ajay Divakaran, Uri Hasson, and Mohamed R Amer · 2019
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Krzysztof J. Geras, Stacey Wolfson, S. Gene Kim, Linda Moy, and Kyunghyun Cho · 2017
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Accurate, large minibatch sgd: Training imagenet in 1 hour
Priya Goyal, Piotr Dollár, Ross Girshick, Pieter Noordhuis, Lukasz Wesolowski, Aapo Kyrola, Andrew Tulloch, Yangqing Jia, and Kaiming He · 2017
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On calibration of modern neural networks
Chuan Guo, Geoff Pleiss, Yu Sun, and Kilian Q Weinberger · 2017
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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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SGDR: stochastic gradient descent with warm restarts
Ilya Loshchilov and Frank Hutter · 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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When does label smoothing help?
Rafael Müller, Simon Kornblith, and Geoffrey E Hinton · 2019
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On variational bounds of mutual information
Ben Poole, Sherjil Ozair, Aäron van den Oord, Alex Alemi, and George Tucker · 2019
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Can you trust your model’s uncertainty? evaluating predictive uncertainty under dataset shift
Jasper Snoek, Yaniv Ovadia, Emily Fertig, Balaji Lakshminarayanan, Sebastian Nowozin, D. Sculley, Joshua V. Dillon, Jie Ren, and Zachary Nado · 2019
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Understanding the limitations of variational mutual information estimators
Jiaming Song and Stefano Ermon · 2019
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Regularized mutual information neural estimation
Kwanghee Choi and Siyeong Lee · 2020
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A unified view of label shift estimation
Saurabh Garg, Yifan Wu, Sivaraman Balakrishnan, and Zachary C. Lipton · 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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M2m: Imbalanced classification via major-to-minor translation
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Formal limitations on the measurement of mutual information
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Balanced meta-softmax for long-tailed visual recognition
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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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Posterior re-calibration for imbalanced datasets
Junjiao Tian, Yen-Cheng Liu, Nathaniel Glaser, Yen-Chang Hsu, and Zsolt Kira · 2020
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Deep generative model for robust imbalance classification
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Rethinking the value of labels for improving class-imbalanced learning
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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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