Fetching the paper…
Reading the bibliography…
Class imbalance is a long-standing problem relevant to a number of real-world applications of deep learning.
Addressing the curse of imbalanced training sets: one-sided selection
M. Kubat, S. Matwin, et al · 1997
Earlier work this paper cites.
Gradient-based learning applied to document recognition
Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner · 1998
Earlier work this paper cites.
Smote: synthetic minority over-sampling technique
N. V. Chawla, K. W. Bowyer, L. O. Hall, and W. P. Kegelmeyer · 2002
Earlier work this paper cites.
Smoteboost: Improving prediction of the minority class in boosting
N. V. Chawla, A. Lazarevic, L. O. Hall, and K. W. Bowyer · 2003
Earlier work this paper cites.
Borderline-smote: A new over-sampling method in imbalanced data sets learning
H. Han, W.-Y. Wang, and B.-H. Mao · 2005
Earlier work this paper cites.
ADASYN: Adaptive synthetic sampling approach for imbalanced learning
H. He, Y. Bai, E. A. Garcia, and S. Li · 2008
Earlier work this paper cites.
Training neural network classifiers for medical decision making: The effects of imbalanced datasets on classification performance
M. A. Mazurowski, P. A. Habas, J. M. Zurada, J. Y. Lo, J. A. Baker, and G. D. Tourassi · 2008
Earlier work this paper cites.
Safe-level-smote: Safe-level-synthetic minority over-sampling technique for handling the class imbalanced problem
C. Bunkhumpornpat, K. Sinapiromsaran, and C. Lursinsap · 2009
Earlier work this paper cites.
Learning from imbalanced data
H. He and E. A. Garcia · 2009
Earlier work this paper cites.
Learning multiple layers of features from tiny images
A. Krizhevsky · 2009
Earlier work this paper cites.
A systematic analysis of performance measures for classification tasks
M. Sokolova and G. Lapalme · 2009
Earlier work this paper cites.
Sun database: Large-scale scene recognition from abbey to zoo
J. Xiao, J. Hays, K. A. Ehinger, A. Oliva, and A. Torralba · 2010
Earlier work this paper cites.
Reading digits in natural images with unsupervised feature learning
Y. Netzer, T. Wang, A. Coates, A. Bissacco, B. Wu, and A. Y. Ng · 2011
Earlier work this paper cites.
Auto-encoding variational bayes
D. P. Kingma and M. Welling · 2013
Earlier work this paper cites.
Dynamic sampling approach to training neural networks for multiclass imbalance classification
M. Lin, K. Tang, and X. Yao · 2013
Earlier work this paper cites.
Mwmote–majority weighted minority oversampling technique for imbalanced data set learning
S. Barua, M. M. Islam, X. Yao, and K. Murase · 2014
Earlier work this paper cites.
Generative adversarial nets
I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio · 2014
Earlier work this paper cites.
Conditional generative adversarial nets
M. Mirza and S. Osindero · 2014
Cited alongside, same era.
Stochastic backpropagation and approximate inference in deep generative models
D. J. Rezende, S. Mohamed, and D. Wierstra · 2014
Cited alongside, same era.
Unsupervised representation learning with deep convolutional generative adversarial networks
A. Radford, L. Metz, and S. Chintala · 2015
Cited alongside, same era.
Unsupervised and semi-supervised learning with categorical generative adversarial networks
J. T. Springenberg · 2015
Cited alongside, same era.
Holistically-nested edge detection
S. Xie and Z. Tu · 2015
Cited alongside, same era.
Gans trained by a two time-scale update rule converge to a local nash equilibrium
M. Heusel, H. Ramsauer, T. Unterthiner, B. Nessler, and S. Hochreiter · 2017
Later among the works it cites.
Semi-supervised learning with gans: Manifold invariance with improved inference
A. Kumar, P. Sattigeri, and T. Fletcher · 2017
Later among the works it cites.
Focal loss for dense object detection
T. Lin, P. Goyal, R. Girshick, K. He, and P. Dollár · 2017
Later among the works it cites.
Least squares generative adversarial networks
X. Mao, Q. Li, H. Xie, R. Y. Lau, Z. Wang, and S. Paul Smolley · 2017
Later among the works it cites.
Conditional image synthesis with auxiliary classifier gans
A. Odena, C. Olah, and J. Shlens · 2017
Later among the works it cites.
Veegan: Reducing mode collapse in gans using implicit variational learning
A. Srivastava, L. Valkov, C. Russell, M. U. Gutmann, and C. Sutton · 2017
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Deep learning for imbalanced multimedia data classification
Y. Yan, M. Chen, M. Shyu, and S. Chen · 2015
Cited alongside, same era.
Lsun: Construction of a large-scale image dataset using deep learning with humans in the loop
F. Yu, A. Seff, Y. Zhang, S. Song, T. Funkhouser, and J. Xiao · 2015
Cited alongside, same era.
A survey of predictive modeling on imbalanced domains
P. Branco, L. Torgo, and R. P. Ribeiro · 2016
Cited alongside, same era.
Cost-aware pre-training for multiclass cost-sensitive deep learning
Y.-A. Chung, H.-T. Lin, and S.-W. Yang · 2016
Cited alongside, same era.
Learning deep representation for imbalanced classification
C. Huang, Y. Li, C. Change Loy, and X. Tang · 2016
Cited alongside, same era.
Learning from imbalanced data: open challenges and future directions
B. Krawczyk · 2016
Cited alongside, same era.
Improved techniques for training gans
T. Salimans, I. Goodfellow, W. Zaremba, V. Cheung, A. Radford, and X. Chen · 2016
Cited alongside, same era.
Later among the works it cites.
Learning to model the tail
Y.-X. Wang, D. Ramanan, and M. Hebert · 2017
Later among the works it cites.
Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms
H. Xiao, K. Rasul, and R. Vollgraf · 2017
Later among the works it cites.
Holistically-nested edge detection
S. Xie and Z. Tu · 2017
Later among the works it cites.
A systematic study of the class imbalance problem in convolutional neural networks
M. Buda, A. Maki, and M. A. Mazurowski · 2018
Later among the works it cites.
Handling data irregularities in classification: Foundations, trends, and future challenges
S. Das, S. Datta, and B. B. Chaudhuri · 2018
Later among the works it cites.
Imbalanced deep learning by minority class incremental rectification
Q. Dong, S. Gong, and X. Zhu · 2018
Later among the works it cites.
Effective data generation for imbalanced learning using conditional generative adversarial networks
G. Douzas and F. Bacao · 2018
Later among the works it cites.
Smote for learning from imbalanced data: Progress and challenges, marking the 15-year anniversary
A. Fernández, S. Garcia, F. Herrera, and N. V. Chawla · 2018
Later among the works it cites.
Cost-sensitive learning of deep feature representations from imbalanced data
S. H. Khan, M. Hayat, M. Bennamoun, F. A. Sohel, and R. Togneri · 2018
Later among the works it cites.
A classification-based study of covariate shift in gan distributions
S. Santurkar, L. Schmidt, and A. Madry · 2018
Later among the works it cites.
Memory replay gans: Learning to generate new categories without forgetting
C. Wu, L. Herranz, X. Liu, J. van de Weijer, B. Raducanu, et al · 2018
Later among the works it cites.