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Neural Networks can perform poorly when the training label distribution is heavily imbalanced, as well as when the testing data differs from the training distribution.
Neural network classification and prior class probabilities
Steve Lawrence, Ian Burns, Andrew Back, Ah Chung Tsoi, and C Lee Giles · 1998
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Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods
John Platt et al · 1999
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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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Adjusting the outputs of a classifier to new a priori probabilities: a simple procedure
Marco Saerens, Patrice Latinne, and Christine Decaestecker · 2002
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Experimental perspectives on learning from imbalanced data
Jason Van Hulse, Taghi M Khoshgoftaar, and Amri Napolitano · 2007
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Adasyn: Adaptive synthetic sampling approach for imbalanced learning
Haibo He, Yang Bai, Edwardo A Garcia, and Shutao Li · 2008
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A kullback-leibler view of linear and log-linear pools
Ali E Abbas · 2009
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A unifying view on dataset shift in classification
Jose G Moreno-Torres, Troy Raeder, RocíO Alaiz-RodríGuez, Nitesh V Chawla, and Francisco Herrera · 2012
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Predicting depth, surface normals and semantic labels with a common multi-scale convolutional architecture
David Eigen and Rob Fergus · 2015
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Faster r-cnn: Towards real-time object detection with region proposal networks
Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun · 2015
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Logistic regression: relating patient characteristics to outcomes
Juliana Tolles and William J Meurer · 2016
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Rethinking the inception architecture for computer vision
Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jon Shlens, and Zbigniew Wojna · 2016
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The synthia dataset: A large collection of synthetic images for semantic segmentation of urban scenes
German Ros, Laura Sellart, Joanna Materzynska, David Vazquez, and Antonio M Lopez · 2016
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The SYNTHIA Dataset: A large collection of synthetic images for semantic segmentation of urban scenes
German Ros, Laura Sellart, Joanna Materzynska, David Vazquez, and Antonio Lopez · 2016
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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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The inaturalist species classification and detection dataset
Grant Van Horn, Oisin Mac Aodha, Yang Song, Yin Cui, Chen Sun, Alex Shepard, Hartwig Adam, Pietro Perona, and Serge Belongie · 2018
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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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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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Survey on deep learning with class imbalance
Justin M Johnson and Taghi M Khoshgoftaar · 2019
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Bbn: Bilateral-branch network with cumulative learning for long-tailed visual recognition
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Segnet: A deep convolutional encoder-decoder architecture for image segmentation
Vijay Badrinarayanan, Alex Kendall, and Roberto Cipolla · 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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On calibration of modern neural networks
Chuan Guo, Geoff Pleiss, Yu Sun, and Kilian Q Weinberger · 2017
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Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs
Liang-Chieh Chen, George Papandreou, Iasonas Kokkinos, Kevin Murphy, and Alan L Yuille · 2017
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Boyan Zhou, Quan Cui, Xiu-Shen Wei, and Zhao-Min Chen · 2019
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Application of decision rules for handling class imbalance in semantic segmentation
Robin Chan, Matthias Rottmann, Fabian Hüger, Peter Schlicht, and Hanno Gottschalk · 2019
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Uno: Uncertainty-aware noisy-or multimodal fusion for unanticipated input degradation
Junjiao Tian, Wesley Cheung, Nathan Glaser, Yen-Cheng Liu, and Zsolt Kira · 2019
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