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Real-world data often exhibit imbalanced distributions, where certain target values have significantly fewer observations.
On estimation of a probability density function and mode
Parzen, E · 1962
Earlier work this paper cites.
The mos 36-item short-form health survey (sf-36): I. conceptual framework and item selection
Ware Jr, J. E. and Sherbourne, C. D · 1992
Earlier work this paper cites.
The sleep heart health study: design, rationale, and methods
Quan, S. F., Howard, B. V., Iber, C., Kiley, J. P., Nieto, F. J., O’Connor, G. T., Rapoport, D. M., Redline, S., Robbins, J., Samet, J. M., et al · 1997
Earlier work this paper cites.
Smote: synthetic minority over-sampling technique
Chawla, N. V., Bowyer, K. W., Hall, L. O., and Kegelmeyer, W. P · 2002
Earlier work this paper cites.
Nltk: The natural language toolkit
Loper, E. and Bird, S · 2002
Earlier work this paper cites.
Adasyn: Adaptive synthetic sampling approach for imbalanced learning
He, H., Bai, Y., Garcia, E. A., and Li, S · 2008
Earlier work this paper cites.
Evolutionary undersampling for classification with imbalanced datasets: Proposals and taxonomy
García, S. and Herrera, F · 2009
Earlier work this paper cites.
Learning multiple layers of features from tiny images
Krizhevsky, A., Hinton, G., et al · 2009
Earlier work this paper cites.
Indoor segmentation and support inference from rgbd images
Nathan Silberman, Derek Hoiem, P. K. and Fergus, R · 2012
Earlier work this paper cites.
Smote for regression
Torgo, L., Ribeiro, R. P., Pfahringer, B., and Branco, P · 2013
Earlier work this paper cites.
Depth map prediction from a single image using a multi-scale deep network
Eigen, D., Puhrsch, C., and Fergus, R · 2014
Earlier work this paper cites.
Adam: A method for stochastic optimization
Kingma, D. P. and Ba, J · 2014
Earlier work this paper cites.
Glove: Global vectors for word representation
Pennington, J., Socher, R., and Manning, C. D · 2014
Earlier work this paper cites.
Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., and Sun, J · 2016
Earlier work this paper cites.
Learning deep representation for imbalanced classification
Huang, C., Li, Y., Change Loy, C., and Tang, X · 2016
Cited alongside, same era.
Return of frustratingly easy domain adaptation
Sun, B., Feng, J., and Saenko, K · 2016
Cited alongside, same era.
Smogn: a pre-processing approach for imbalanced regression
Branco, P., Torgo, L., and Ribeiro, R. P · 2017
Cited alongside, same era.
Semeval-2017 task 1: Semantic textual similarity multilingual and crosslingual focused evaluation
Cer, D., Diab, M., Agirre, E., Lopez-Gazpio, I., and Specia, L · 2017
Cited alongside, same era.
Allennlp: A deep semantic natural language processing platform
Gardner, M., Grus, J., Neumann, M., Tafjord, O., Dasigi, P., Liu, N. F., Peters, M., Schmitz, M., and Zettlemoyer, L. S · 2017
Cited alongside, same era.
Focal loss for dense object detection
Lin, T.-Y., Goyal, P., Girshick, R., He, K., and Dollár, P · 2017
Learning imbalanced datasets with label-distribution-aware margin loss
Cao, K., Wei, C., Gaidon, A., Arechiga, N., and Ma, T · 2019
Later among the works it cites.
Class-balanced loss based on effective number of samples
Cui, Y., Jia, M., Lin, T.-Y., Song, Y., and Belongie, S · 2019
Later among the works it cites.
Imbalanced deep learning by minority class incremental rectification
Dong, Q., Gong, S., and Zhu, X · 2019
Later among the works it cites.
Revisiting single image depth estimation: Toward higher resolution maps with accurate object boundaries
Hu, J., Ozay, M., Zhang, Y., and Okatani, T · 2019
Later among the works it cites.
Deep imbalanced learning for face recognition and attribute prediction
Huang, C., Li, Y., Chen, C. L., and Tang, X · 2019
Later among the works it cites.
Large-scale long-tailed recognition in an open world
Liu, Z., Miao, Z., Zhan, X., Wang, J., Gong, B., and Yu, S. X · 2019
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Agedb: The first manually collected, in-the-wild age database
Moschoglou, S., Papaioannou, A., Sagonas, C., Deng, J., Kotsia, I., and Zafeiriou, S · 2017
Cited alongside, same era.
Range loss for deep face recognition with long-tailed training data
Zhang, X., Fang, Z., Wen, Y., Li, Z., and Qiao, Y · 2017
Cited alongside, same era.
Rebagg: Resampled bagging for imbalanced regression
Branco, P., Torgo, L., and Ribeiro, R. P · 2018
Cited alongside, same era.
A systematic study of the class imbalance problem in convolutional neural networks
Buda, M., Maki, A., and Mazurowski, M. A · 2018
Cited alongside, same era.
Simple recurrent units for highly parallelizable recurrence
Lei, T., Zhang, Y., Wang, S. I., Dai, H., and Artzi, Y · 2018
Cited alongside, same era.
Deep expectation of real and apparent age from a single image without facial landmarks
Rothe, R., Timofte, R., and Gool, L. V · 2018
Cited alongside, same era.
Later among the works it cites.
Meta-weight-net: Learning an explicit mapping for sample weighting
Shu, J., Xie, Q., Yi, L., Zhao, Q., Zhou, S., Xu, Z., and Meng, D · 2019
Later among the works it cites.
Manifold mixup: Better representations by interpolating hidden states
Verma, V., Lamb, A., Beckham, C., Najafi, A., Mitliagkas, I., Lopez-Paz, D., and Bengio, Y · 2019
Later among the works it cites.
Bidirectional inference networks: A class of deep bayesian networks for health profiling
Wang, H., Mao, C., He, H., Zhao, M., Jaakkola, T. S., and Katabi, D · 2019
Later among the works it cites.
Feature transfer learning for face recognition with under-represented data
Yin, X., Yu, X., Sohn, K., Liu, X., and Chandraker, M · 2019
Later among the works it cites.
Adabins: Depth estimation using adaptive bins
Bhat, S. F., Alhashim, I., and Wonka, P · 2020
Later among the works it cites.
Decoupling representation and classifier for long-tailed recognition
Kang, B., Xie, S., Rohrbach, M., Yan, Z., Gordo, A., Feng, J., and Kalantidis, Y · 2020
Later among the works it cites.
Long-tailed classification by keeping the good and removing the bad momentum causal effect
Tang, K., Huang, J., and Zhang, H · 2020
Later among the works it cites.
Rethinking the value of labels for improving class-imbalanced learning
Yang, Y. and Xu, Z · 2020
Later among the works it cites.