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Real-world data often exhibit imbalanced label distributions.
Multidimensional scaling
J. D. Carroll and P. Arabie · 1998
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.
An overview of statistical learning theory
V. N. Vapnik · 1999
Earlier work this paper cites.
The mahalanobis distance
R. De Maesschalck, D. Jouan-Rimbaud, and D. L. Massart · 2000
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.
Euclidean embedding of co-occurrence data
A. Globerson, G. Chechik, F. Pereira, and N. Tishby · 2004
Earlier work this paper cites.
Neighbourhood components analysis
J. Goldberger, G. E. Hinton, S. Roweis, and R. R. Salakhutdinov · 2004
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.
A survey on transfer learning
S. J. Pan and Q. Yang · 2009
Earlier work this paper cites.
A theory of learning from different domains
S. Ben-David, J. Blitzer, K. Crammer, A. Kulesza, F. Pereira, and J. W. Vaughan · 2010
Earlier work this paper cites.
Multi-domain learning by confidence-weighted parameter combination
M. Dredze, A. Kulesza, and K. Crammer · 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.
A kernel two-sample test
A. Gretton, K. M. Borgwardt, M. J. Rasch, B. Schölkopf, and A. Smola · 2012
Earlier work this paper cites.
Unbiased metric learning: On the utilization of multiple datasets and web images for softening bias
C. Fang, Y. Xu, and D. N. Rockmore · 2013
Earlier work this paper cites.
Domain generalization via invariant feature representation
K. Muandet, D. Balduzzi, and B. Schölkopf · 2013
Earlier work this paper cites.
Adam: A method for stochastic optimization
D. P. Kingma and J. Ba · 2015
Earlier work this paper cites.
A unified perspective on multi-domain and multi-task learning
Y. Yang and T. M. Hospedales · 2015
Earlier work this paper cites.
Domain-adversarial training of neural networks
Y. Ganin, E. Ustinova, H. Ajakan, P. Germain, H. Larochelle, F. Laviolette, M. Marchand, and V. Lempitsky · 2016
Earlier work this paper cites.
Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
Cited alongside, same era.
Improved deep metric learning with multi-class n-pair loss objective
K. Sohn · 2016
Cited alongside, same era.
Deep coral: Correlation alignment for deep domain adaptation
B. Sun and K. Saenko · 2016
Cited alongside, same era.
Learning deep feature representations with domain guided dropout for person re-identification
T. Xiao, H. Li, W. Ouyang, and X. Wang · 2016
Cited alongside, same era.
Deeper, broader and artier domain generalization
D. Li, Y. Yang, Y.-Z. Song, and T. M. Hospedales · 2017
Cited alongside, same era.
Focal loss for dense object detection
T.-Y. Lin, P. Goyal, R. Girshick, K. He, and P. Dollár · 2017
Cited alongside, same era.
Multi-domain adversarial learning
A. Schoenauer-Sebag, L. Heinrich, M. Schoenauer, M. Sebag, L. F. Wu, and S. J. Altschuler · 2019
Later among the works it cites.
Meta-weight-net: Learning an explicit mapping for sample weighting
J. Shu, Q. Xie, L. Yi, Q. Zhao, S. Zhou, Z. Xu, and D. Meng · 2019
Later among the works it cites.
Decoupling representation and classifier for long-tailed recognition
B. Kang, S. Xie, M. Rohrbach, Z. Yan, A. Gordo, J. Feng, and Y. Kalantidis · 2020
Later among the works it cites.
Out-of-distribution generalization via risk extrapolation (rex)
D. Krueger, E. Caballero, J.-H. Jacobsen, A. Zhang, J. Binas, R. L. Priol, and A. Courville · 2020
Later among the works it cites.
Balanced meta-softmax for long-tailed visual recognition
J. Ren, C. Yu, X. Ma, H. Zhao, S. Yi, et al · 2020
Later among the works it cites.
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Range loss for deep face recognition with long-tailed training data
X. Zhang, Z. Fang, Y. Wen, Z. Li, and Y. Qiao · 2017
Cited alongside, same era.
Recognition in terra incognita
S. Beery, G. Van Horn, and P. Perona · 2018
Cited alongside, same era.
A systematic study of the class imbalance problem in convolutional neural networks
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Cited alongside, same era.
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Cited alongside, same era.
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Cited alongside, same era.
Distributionally robust neural networks for group shifts: On the importance of regularization for worst-case generalization
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Later among the works it cites.
Adversarial domain adaptation with domain mixup
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Rethinking the value of labels for improving class-imbalanced learning
Y. Yang and Z. Xu · 2020
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Adaptive risk minimization: A meta-learning approach for tackling group shift
M. Zhang, H. Marklund, A. Gupta, S. Levine, and C. Finn · 2020
Later among the works it cites.
Bbn: Bilateral-branch network with cumulative learning for long-tailed visual recognition
B. Zhou, Q. Cui, X.-S. Wei, and Z.-M. Chen · 2020
Later among the works it cites.
Domain generalization by marginal transfer learning
G. Blanchard, A. A. Deshmukh, U. Dogan, G. Lee, and C. Scott · 2021
Later among the works it cites.
In search of lost domain generalization
I. Gulrajani and D. Lopez-Paz · 2021
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Targeted supervised contrastive learning for long-tailed recognition
T. Li, P. Cao, Y. Yuan, L. Fan, Y. Yang, R. Feris, P. Indyk, and D. Katabi · 2021
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Reducing domain gap by reducing style bias
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Gradient matching for domain generalization
Y. Shi, J. Seely, P. H. Torr, N. Siddharth, A. Hannun, N. Usunier, and G. Synnaeve · 2021
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Long-tailed recognition by routing diverse distribution-aware experts
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Delving into deep imbalanced regression
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