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Supervised learning from training data with imbalanced class sizes, a commonly encountered scenario in real applications such as anomaly/fraud detection, has long been considered a significant challenge in machine learning.
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Smote: synthetic minority over-sampling technique
N. V. Chawla, K. W. Bowyer, L. O. Hall, and W. P. Kegelmeyer · 2002
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Self-paced learning for latent variable models
M. P. Kumar, B. Packer, and D. Koller · 2010
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Finding rare classes: Active learning with generative and discriminative models
T. M. Hospedales, S. Gong, and T. Xiang · 2011
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Local neighbourhood extension of smote for mining imbalanced data
T. Maciejewski and J. Stefanowski · 2011
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Finding rare classes: Active learning with generative and discriminative models
T. M. Hospedales, S. Gong, and T. Xiang · 2013
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Pseudo-label: The simple and efficient semi-supervised learning method for deep neural networks, 2013
D.-h. Lee · 2013
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Learning and transferring mid-level image representations using convolutional neural networks
M. Oquab, L. Bottou, I. Laptev, and J. Sivic · 2014
Mentornet: Learning data-driven curriculum for very deep neural networks on corrupted labels
L. Jiang, Z. Zhou, T. Leung, L.-J. Li, and L. Fei-Fei · 2017
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Focal loss for dense object detection
T.-Y. Lin, P. Goyal, R. Girshick, K. He, and P. Dollár · 2017
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Learning to model the tail
Y.-X. Wang, D. Ramanan, and M. Hebert · 2017
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Semi-supervised deep learning using pseudo labels for hyperspectral image classification
H. Wu and S. Prasad · 2017
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A systematic study of the class imbalance problem in convolutional neural networks
M. Buda, A. Maki, and M. A. Mazurowski · 2018
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Imbalanced deep learning by minority class incremental rectification
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Self-paced curriculum learning
L. Jiang, D. Meng, Q. Zhao, S. Shan, and A. G. Hauptmann · 2015
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Learning deep representation for imbalanced classification
C. Huang, Y. Li, C. Change Loy, and X. Tang · 2016
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Class rectification hard mining for imbalanced deep learning
Q. Dong, S. Gong, and X. Zhu · 2017
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Q. Dong, S. Gong, and X. Zhu · 2018
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Dimensionality-driven learning with noisy labels
X. Ma, Y. Wang, M. E. Houle, S. Zhou, S. Erfani, S. Xia, S. Wijewickrema, and J. Bailey · 2018
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Curriculum learning by transfer learning: Theory and experiments with deep networks
D. Weinshall, G. Cohen, and D. Amir · 2018
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Sparc: Self-paced network representation for few-shot rare category characterization
D. Zhou, J. He, H. Yang, and W. Fan · 2018
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