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Recently, deep learning models have achieved great success in computer vision applications, relying on large-scale class-balanced datasets.
S. Kullback and R. A. Leibler, “On information and sufficiency,”
1951
Earlier work this paper cites.
N. V. Chawla, K. W. Bowyer, L. O. Hall, and W. P. Kegelmeyer, “Smote: synthetic minority over-sampling technique,”
2002
Earlier work this paper cites.
C. Drummond, R. C. Holte,
2003
Earlier work this paper cites.
G. E. Batista, R. C. Prati, and M. C. Monard, “A study of the behavior of several methods for balancing machine learning training data,”
2004
Earlier work this paper cites.
H. He, Y. Bai, E. A. Garcia, and S. Li, “Adasyn: Adaptive synthetic sampling approach for imbalanced learning,” in
2008
Earlier work this paper cites.
S.-J. Yen and Y.-S. Lee, “Cluster-based under-sampling approaches for imbalanced data distributions,”
2009
Earlier work this paper cites.
A. Krizhevsky and G. Hinton, “Learning multiple layers of features from tiny images,” in
2009
Earlier work this paper cites.
J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei, “Imagenet: A large-scale hierarchical image database,” in
2009
Earlier work this paper cites.
K. H. Brodersen, C. S. Ong, K. E. Stephan, and J. M. Buhmann, “The balanced accuracy and its posterior distribution,” in
2010
Earlier work this paper cites.
A. Krizhevsky, I. Sutskever, and G. E. Hinton, “Imagenet classification with deep convolutional neural networks,” in
2012
Earlier work this paper cites.
L. Piras and G. Giacinto, “Synthetic pattern generation for imbalanced learning in image retrieval,”
2012
Earlier work this paper cites.
T. Wu and A. Ranganathan, “A practical system for road marking detection and recognition,” in
2012
Earlier work this paper cites.
K. Simonyan and A. Zisserman, “Very deep convolutional networks for large-scale image recognition,”
2014
Earlier work this paper cites.
I. Sutskever, O. Vinyals, and Q. V. Le, “Sequence to sequence learning with neural networks,”
2014
Earlier work this paper cites.
D. Bahdanau, K. Cho, and Y. Bengio, “Neural machine translation by jointly learning to align and translate,”
2014
Earlier work this paper cites.
C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich, “Going deeper with convolutions,” in
2015
Earlier work this paper cites.
K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in
2016
Cited alongside, same era.
Q. Fan, Z. Wang, and D. Gao, “One-sided dynamic undersampling no-propagation neural networks for imbalance problem,”
2016
Cited alongside, same era.
M. Zeng, B. Zou, F. Wei, X. Liu, and L. Wang, “Effective prediction of three common diseases by combining smote with tomek links technique for imbalanced medical data,” in
2016
Cited alongside, same era.
F. N. Iandola, S. Han, M. W. Moskewicz, K. Ashraf, W. J. Dally, and K. Keutzer, “Squeezenet: Alexnet-level accuracy with 50x fewer parameters and¡ 0.5 mb model size,”
2016
Cited alongside, same era.
B. Tang and H. He, “Gir-based ensemble sampling approaches for imbalanced learning,”
2017
Cited alongside, same era.
Y.-g. Kim, Y. Kwon, and M. C. Paik, “Valid oversampling schemes to handle imbalance,”
2019
Later among the works it cites.
P. Sadhukhan and S. Palit, “Reverse-nearest neighborhood based oversampling for imbalanced, multi-label datasets,”
2019
Later among the works it cites.
S. Shafieezadeh-Abadeh, D. Kuhn, and P. M. Esfahani, “Regularization via mass transportation.,”
2019
Later among the works it cites.
S. Ryou, S.-G. Jeong, and P. Perona, “Anchor loss: Modulating loss scale based on prediction difficulty,” in
2019
Later among the works it cites.
H.-Y. Chen, P.-H. Wang, C.-H. Liu, S.-C. Chang, J.-Y. Pan, Y.-T. Chen, W. Wei, and D.-C. Juan, “Complement objective training,”
2019
Later among the works it cites.
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T.-Y. Lin, P. Goyal, R. Girshick, K. He, and P. Dollár, “Focal loss for dense object detection,” in
2017
Cited alongside, same era.
G. Pereyra, G. Tucker, J. Chorowski, Ł. Kaiser, and G. Hinton, “Regularizing neural networks by penalizing confident output distributions,”
2017
Cited alongside, same era.
S. Xie, R. Girshick, P. Dollár, Z. Tu, and K. He, “Aggregated residual transformations for deep neural networks,” in
2017
Cited alongside, same era.
G. Huang, Z. Liu, L. Van Der Maaten, and K. Q. Weinberger, “Densely connected convolutional networks,” in
2017
Cited alongside, same era.
H. Xiao, K. Rasul, and R. Vollgraf, “Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms,”
2017
Cited alongside, same era.
O. Bailo, S. Lee, F. Rameau, J. S. Yoon, and I. S. Kweon, “Robust road marking detection and recognition using density-based grouping and machine learning techniques,” in
2017
Cited alongside, same era.
T. Ahmad, D. Ilstrup, E. Emami, and G. Bebis, “Symbolic road marking recognition using convolutional neural networks,” in
2017
Cited alongside, same era.
2019
Later among the works it cites.
T. He, Z. Zhang, H. Zhang, Z. Zhang, J. Xie, and M. Li, “Bag of tricks for image classification with convolutional neural networks,” in
2019
Later among the works it cites.
M. Tan and Q. V. Le, “Efficientnet: Rethinking model scaling for convolutional neural networks,”
2019
Later among the works it cites.
Y. Lee, J. Lee, Y. Hong, Y. Ko, and M. Jeon, “Unconstrained road marking recognition with generative adversarial networks,” in
2019
Later among the works it cites.
Y. Lee, H. Yoo, Y. Kim, J. Jeong, and M. Jeon, “Self-supervised attribute-aware refinement network for low-quality text recognition,” in
2020
Closest in time.
M. Koziarski, “Radial-based undersampling for imbalanced data classification,”
2020
Closest in time.
B. Liu and G. Tsoumakas, “Dealing with class imbalance in classifier chains via random undersampling,”
2020
Closest in time.
Y. Zhu, C. Jia, F. Li, and J. Song, “Inspector: a lysine succinylation predictor based on edited nearest-neighbor undersampling and adaptive synthetic oversampling,”
2020
Closest in time.
C. Wang, C. Deng, and S. Wang, “Imbalance-xgboost: leveraging weighted and focal losses for binary label-imbalanced classification with xgboost,”
2020
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Y. Netzer, T. Wang, A. Coates, A. Bissacco, B. Wu, and A. Y. Ng, “Reading digits in natural images with unsupervised feature learning,”
2020
Closest in time.