Fetching the paper…
Reading the bibliography…
Class imbalance is a common problem in the case of real-world object detection and classification tasks.
S. H. Khan, M. Bennamoun, F. Sohel, and R. Togneri, “Automatic feature learning for robust shadow detection,” in
1946
Earlier work this paper cites.
V. Garcia, J. Sanchez, J. Mollineda, R. Alejo, and J. Sotoca, “The class imbalance problem in pattern classification and learning,” in
1946
Earlier work this paper cites.
M. Kukar, I. Kononenko
1998
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.
G. Wu and E. Y. Chang, “Class-boundary alignment for imbalanced dataset learning,” in
2003
Earlier work this paper cites.
I. Mani and I. Zhang, “Knn approach to unbalanced data distributions: a case study involving information extraction,” in
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.
K. Huang, H. Yang, I. King, and M. R. Lyu, “Learning classifiers from imbalanced data based on biased minimax probability machine,” in
2004
Earlier work this paper cites.
C. Chen, A. Liaw, and L. Breiman, “Using random forest to learn imbalanced data,”
2004
Earlier work this paper cites.
2004
Earlier work this paper cites.
H. Han, W.-Y. Wang, and B.-H. Mao, “Borderline-smote: a new over-sampling method in imbalanced data sets learning,” in
2005
Earlier work this paper cites.
——, “Kba: Kernel boundary alignment considering imbalanced data distribution,”
2005
Earlier work this paper cites.
R. Espíndola and N. Ebecken, “On extending f-measure and g-mean metrics to multi-class problems,”
2005
Earlier work this paper cites.
Z.-H. Zhou and X.-Y. Liu, “Training cost-sensitive neural networks with methods addressing the class imbalance problem,”
2006
Earlier work this paper cites.
P. L. Bartlett, M. I. Jordan, and J. D. McAuliffe, “Convexity, classification, and risk bounds,”
2006
Earlier work this paper cites.
C. Bunkhumpornpat, K. Sinapiromsaran, and C. Lursinsap, “Safe-level-smote: Safe-level-synthetic minority over-sampling technique for handling the class imbalanced problem,” in
2009
Earlier work this paper cites.
A. Vedaldi, V. Gulshan, M. Varma, and A. Zisserman, “Multiple kernels for object detection,” in
2009
Earlier work this paper cites.
J. Yang, K. Yu, Y. Gong, and T. Huang, “Linear spatial pyramid matching using sparse coding for image classification,” in
2009
Earlier work this paper cites.
Y. Tang, Y.-Q. Zhang, N. V. Chawla, and S. Krasser, “Svms modeling for highly imbalanced classification,”
2009
Earlier work this paper cites.
H. He and E. A. Garcia, “Learning from imbalanced data,”
2009
Earlier work this paper cites.
P. Jeatrakul, K. W. Wong, and C. C. Fung, “Classification of imbalanced data by combining the complementary neural network and smote algorithm,” in
2010
Earlier work this paper cites.
B. X. Wang and N. Japkowicz, “Boosting support vector machines for imbalanced data sets,”
2010
Cited alongside, same era.
J. Wang, J. Yang, K. Yu, F. Lv, T. Huang, and Y. Gong, “Locality-constrained linear coding for image classification,” in
2010
Cited alongside, same era.
F. Perronnin, J. Sánchez, and T. Mensink, “Improving the fisher kernel for large-scale image classification,” in
2010
Cited alongside, same era.
T. Maciejewski and J. Stefanowski, “Local neighbourhood extension of smote for mining imbalanced data,” in
2011
Cited alongside, same era.
K. Chatfield, V. Lempitsky, A. Vedaldi, and A. Zisserman, “The devil is in the details: An evaluation of recent feature encoding methods,” 2011
2011
Cited alongside, same era.
Y. Zhang, P. Fu, W. Liu, and G. Chen, “Imbalanced data classification based on scaling kernel-based support vector machine,”
2014
Later among the works it cites.
K. Li, X. Kong, Z. Lu, L. Wenyin, and J. Yin, “Boosting weighted elm for imbalanced learning,”
2014
Later among the works it cites.
R. Girshick, J. Donahue, T. Darrell, and J. Malik, “Rich feature hierarchies for accurate object detection and semantic segmentation,” in
2014
Later among the works it cites.
O. Beijbom, M. Saberian, D. Kriegman, and N. Vasconcelos, “Guess-averse loss functions for cost-sensitive multiclass boosting,” in
2014
Later among the works it cites.
M. A. Nielsen, “Neural networks and deep learning,”
2014
Later among the works it cites.
K. Simonyan and A. Zisserman, “Very deep convolutional networks for large-scale image recognition,”
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2011
Cited alongside, same era.
O. Beijbom, P. J. Edmunds, D. Kline, B. G. Mitchell, D. Kriegman
2012
Cited alongside, same era.
E. Ramentol, Y. Caballero, R. Bello, and F. Herrera, “Smote-rsb*: A hybrid preprocessing approach based on oversampling and undersampling for high imbalanced data-sets using smote and rough sets theory,”
2012
Cited alongside, same era.
A. Krizhevsky, I. Sutskever, and G. E. Hinton, “Imagenet classification with deep convolutional neural networks,” in
2012
Cited alongside, same era.
——, “Non-melanoma skin lesion classification using colour image data in a hierarchical k-nn classifier,” in
2012
Cited alongside, same era.
M. Lin, Q. Chen, and S. Yan, “Network in network,” in
2013
Cited alongside, same era.
Y. Zhang and D. Wang, “A cost-sensitive ensemble method for class-imbalanced datasets,” in
2013
Cited alongside, same era.
2014
Later among the works it cites.
J. T. Springenberg and M. Riedmiller, “Improving deep neural networks with probabilistic maxout units,”
2014
Later among the works it cites.
T.-H. Lin and H. Kung, “Stable and efficient representation learning with nonnegativity constraints,” in
2014
Later among the works it cites.
J. Donahue, Y. Jia, O. Vinyals, J. Hoffman, N. Zhang, E. Tzeng, and T. Darrell, “Decaf: A deep convolutional activation feature for generic visual recognition,” in
2014
Later among the works it cites.
M. D. Zeiler and R. Fergus, “Visualizing and understanding convolutional networks,” in
2014
Later among the works it cites.
2014
Later among the works it cites.
D. Lin, C. Lu, R. Liao, and J. Jia, “Learning important spatial pooling regions for scene classification,” 2014
2014
Later among the works it cites.
A. S. Razavian, H. Azizpour, J. Sullivan, and S. Carlsson, “Cnn features off-the-shelf: An astounding baseline for recognition,” in
2014
Later among the works it cites.
Y. Gong, L. Wang, R. Guo, and S. Lazebnik, “Multi-scale orderless pooling of deep convolutional activation features,” in
2014
Later among the works it cites.
C.-Y. Lee, S. Xie, P. Gallagher, Z. Zhang, and Z. Tu, “Deeply-supervised nets,” 2015
2015
Closest in time.
2015
Closest in time.
2015
Closest in time.
J.-R. Chang and Y.-S. Chen, “Batch-normalized maxout network in network,”
2015
Closest in time.
S. Wang, W. Liu, J. Wu, L. Cao, Q. Meng, and P. J. Kennedy, “Training deep neural networks on imbalanced data sets,” in
2016
Closest in time.
V. Raj, S. Magg, and S. Wermter, “Towards effective classification of imbalanced data with convolutional neural networks,” in
2016
Closest in time.
C.-Y. Lee, P. W. Gallagher, and Z. Tu, “Generalizing pooling functions in convolutional neural networks: Mixed, gated, and tree,” in
2016
Closest in time.