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Robust loss functions are essential for training accurate deep neural networks (DNNs) in the presence of noisy (incorrect) labels.
Wang, X., Hua, Y., Kodirov, E., and Robertson, N. M · 1903
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Wang, X., Kodirov, E., Hua, Y., and Robertson, N. M · 1905
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Gradient-based learning applied to document recognition
LeCun, Y., Bottou, L., Bengio, Y., and Haffner, P · 1998
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Imagenet: A large-scale hierarchical image database
Deng, J., Dong, W., Socher, R., Li, Li, J., Li, K., and Fei-Fei, L · 2009
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Learning multiple layers of features from tiny images
Krizhevsky, A. and Hinton, G · 2009
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Learning with noisy labels
Natarajan, N., Dhillon, I. S., Ravikumar, P. K., and Tewari, A · 2013
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Training deep neural networks on noisy labels with bootstrapping
Reed, S., Lee, H., Anguelov, D., Szegedy, C., Erhan, D., and Rabinovich, A · 2014
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Training convolutional networks with noisy labels
Sukhbaatar, S., Bruna, J., Paluri, M., Bourdev, L., and Fergus, R · 2014
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Learning from massive noisy labeled data for image classification
Xiao, T., Xia, T., Yang, Y., Huang, C., and Wang, X · 2015
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Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., and Sun, J · 2016
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Rethinking the inception architecture for computer vision
Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., and Wojna, Z · 2016
Cited alongside, same era.
Robust loss functions under label noise for deep neural networks
Ghosh, A., Kumar, H., and Sastry, P · 2017
Cited alongside, same era.
Training deep neural-networks using a noise adaptation layer
Goldberger, J. and Ben-Reuven, E · 2017
Cited alongside, same era.
Cleannet: Transfer learning for scalable image classifier training with label noise
Lee, K.-H., He, X., Zhang, L., and Yang, L · 2017
Cited alongside, same era.
Focal loss for dense object detection
Lin, T.-Y., Goyal, P., Girshick, R., He, K., and Dollár, P · 2017
Cited alongside, same era.
Decoupling” when to update” from” how to update”
Malach, E. and Shalev-Shwartz, S · 2017
Mentornet: Learning data-driven curriculum for very deep neural networks on corrupted labels
Jiang, L., Zhou, Z., Leung, T., Li, L.-J., and Fei-Fei, L · 2018
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Dimensionality-driven learning with noisy labels
Ma, X., Wang, Y., Houle, M. E., Zhou, S., Erfani, S. M., Xia, S.-T., Wijewickrema, S., and Bailey, J · 2018
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Joint optimization framework for learning with noisy labels
Tanaka, D., Ikami, D., Yamasaki, T., and Aizawa, K · 2018
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Iterative learning with open-set noisy labels
Wang, Y., Liu, W., Ma, X., Bailey, J., Zha, H., Song, L., and Xia, S.-T · 2018
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Generalized cross entropy loss for training deep neural networks with noisy labels
Zhang, Z. and Sabuncu, M. R · 2018
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On symmetric losses for learning from corrupted labels
Charoenphakdee, N., Lee, J., and Sugiyama, M · 2019
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Cited alongside, same era.
Making neural networks robust to label noise: a loss correction approach
Patrini, G., Rozza, A., Menon, A., Nock, R., and Qu, L · 2017
Cited alongside, same era.
Regularizing neural networks by penalizing confident output distributions
Pereyra, G., Tucker, G., Chorowski, J., Kaiser, L., and Hinton, G · 2017
Cited alongside, same era.
Toward robustness against label noise in training deep discriminative neural networks
Vahdat, A · 2017
Cited alongside, same era.
Learning from noisy large-scale datasets with minimal supervision
Veit, A., Alldrin, N., Chechik, G., Krasin, I., Gupta, A., and Belongie, S · 2017
Cited alongside, same era.
Masking: A new perspective of noisy supervision
Han, B., Yao, J., Niu, G., Zhou, M., Tsang, I., Zhang, Y., and Sugiyama, M
Cited in the paper.
Co-teaching: robust training deep neural networks with extremely noisy labels
Han, B., Yao, Q., Yu, X., Niu, G., Xu, M., Hu, W., Tsang, I., and Sugiyama, M
Cited in the paper.
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Nlnl: Negative learning for noisy labels
Kim, Y., Yim, J., Yun, J., and Kim, J · 2019
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Secost: Sequential co-supervision for weakly labeled audio event detection
Kumar, A. and Ithapu, V. K · 2019
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L_dmi: An information-theoretic noise-robust loss function
Xu, Y., Cao, P., Kong, Y., and Wang, Y · 2019
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How does disagreement help generalization against label corruption?
Yu, X., Han, B., Yao, J., Niu, G., Tsang, I., and Sugiyama, M · 2019
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