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In the presence of noisy labels, designing robust loss functions is critical for securing the generalization performance of deep neural networks.
Learning long-term dependencies with gradient descent is difficult
Bengio, Y., Simard, P., and Frasconi, P · 1994
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Noise-tolerant learning, the parity problem, and the statistical query model
Blum, A., Kalai, A., and Wasserman, H · 2003
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Basic real analysis , volume 231
Sohrab, H. H · 2003
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Imagenet: A large-scale hierarchical image database
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., and Fei-Fei, L · 2009
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Learning multiple layers of features from tiny images
Krizhevsky, A., Hinton, G., et al · 2009
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Metainfonet: Learning task-guided information for sample reweighting
Wei, H., Feng, L., Wang, R., and An, B · 2012
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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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Learning from multiple annotators with varying expertise
Yan, Y., Rosales, R., Fung, G., Subramanian, R., and Dy, J · 2014
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Beyond convexity: Stochastic quasi-convex optimization
Hazan, E., Levy, K., and Shalev-Shwartz, S · 2015
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Classification with noisy labels by importance reweighting
Liu, T. and Tao, D · 2015
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Deep learning with differential privacy
Abadi, M., Chu, A., Goodfellow, I., McMahan, H. B., Mironov, I., Talwar, K., and Zhang, L · 2016
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Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., and Sun, J · 2016
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Squeezenet: Alexnet-level accuracy with 50x fewer parameters and < < 0.5 mb model size
Iandola, F. N., Han, S., Moskewicz, M. W., Ashraf, K., Dally, W. J., and Keutzer, K · 2016
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Temporal ensembling for semi-supervised learning
Laine, S. and Aila, T · 2016
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The power of normalization: Faster evasion of saddle points
Levy, K. Y · 2016
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Rethinking the inception architecture for computer vision
Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., and Wojna, Z · 2016
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Wide residual networks
Zagoruyko, S. and Komodakis, N · 2016
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Understanding deep learning requires rethinking generalization
Zhang, C., Bengio, S., Hardt, M., Recht, B., and Vinyals, O · 2016
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A closer look at memorization in deep networks
Arpit, D., Jastrzebski, S., Ballas, N., Krueger, D., Bengio, E., Kanwal, M. S., Maharaj, T., Fischer, A., Courville, A., Bengio, Y., et al · 2017
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Robust loss functions under label noise for deep neural networks
Ghosh, A., Kumar, H., and Sastry, P · 2017
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Mobilenets: Efficient convolutional neural networks for mobile vision applications
Howard, A. G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., and Adam, H · 2017
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Densely connected convolutional networks
Huang, G., Liu, Z., Van Der Maaten, L., and Weinberger, K. Q · 2017
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Webvision database: Visual learning and understanding from web data
Li, W., Wang, L., Li, W., Agustsson, E., and Van Gool, L · 2017
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Focal loss for dense object detection
Lin, T.-Y., Goyal, P., Girshick, R., He, K., and Dollár, P · 2017
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Decoupling “when to update" from “how to update"
Malach, E. and Shalev-Shwartz, S · 2017
Cited alongside, same era.
Making deep neural networks robust to label noise: A loss correction approach
Patrini, G., Rozza, A., Krishna Menon, A., Nock, R., and Qu, L · 2017
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Co-teaching: Robust training of deep neural networks with extremely noisy labels
Han, B., Yao, Q., Yu, X., Niu, G., Xu, M., Hu, W., Tsang, I., and Sugiyama, M · 2018
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Using trusted data to train deep networks on labels corrupted by severe noise
Hendrycks, D., Mazeika, M., Wilson, D., and Gimpel, K · 2018
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Mentornet: Learning data-driven curriculum for very deep neural networks on corrupted labels
Peer loss functions: Learning from noisy labels without knowing noise rates
Liu, Y. and Guo, H · 2020
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Does label smoothing mitigate label noise?
Lukasik, M., Bhojanapalli, S., Menon, A., and Kumar, S · 2020
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Normalized loss functions for deep learning with noisy labels
Ma, X., Huang, H., Wang, Y., Romano, S., Erfani, S., and Bailey, J · 2020
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Can gradient clipping mitigate label noise?
Menon, A. K., Rawat, A. S., Kumar, S., and Reddi, S · 2020
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Self: Learning to filter noisy labels with self-ensembling
Nguyen, D. T., Mummadi, C. K., Ngo, T. P. N., Nguyen, T. H. P., Beggel, L., and Brox, T · 2020
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Why gradient clipping accelerates training: A theoretical justification for adaptivity
Zhang, J., He, T., Sra, S., and Jadbabaie, A · 2020
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Jiang, L., Zhou, Z., Leung, T., Li, L.-J., and Fei-Fei, L · 2018
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Virtual adversarial training: A regularization method for supervised and semi-supervised learning
Miyato, T., Maeda, S.-i., Koyama, M., and Ishii, S · 2018
Cited alongside, same era.
Learning to reweight examples for robust deep learning
Ren, M., Zeng, W., Yang, B., and Urtasun, R · 2018
Cited alongside, same era.
Joint optimization framework for learning with noisy labels
Tanaka, D., Ikami, D., Yamasaki, T., and Aizawa, K · 2018
Cited alongside, same era.
Generalized cross entropy loss for training deep neural networks with noisy labels
Zhang, Z. and Sabuncu, M · 2018
Cited alongside, same era.
Unsupervised label noise modeling and loss correction
Arazo, E., Ortego, D., Albert, P., O’Connor, N., and McGuinness, K · 2019
Cited alongside, same era.
Hu, W., Li, Z., and Yu, D · 2019
Cited alongside, same era.
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Error-bounded correction of noisy labels
Zheng, S., Wu, P., Goswami, A., Goswami, M., Metaxas, D., and Chen, C · 2020
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Understanding and improving early stopping for learning with noisy labels
Bai, Y., Yang, E., Han, B., Yang, Y., Li, J., Mao, Y., Niu, G., and Liu, T · 2021
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Learning with instance-dependent label noise: A sample sieve approach
Cheng, H., Zhu, Z., Li, X., Gong, Y., Sun, X., and Liu, Y · 2021
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Wasserstein adversarial regularization for learning with label noise
Fatras, K., Damodaran, B. B., Lobry, S., Flamary, R., Tuia, D., and Courty, N · 2021
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Sharpness-aware minimization for efficiently improving generalization
Foret, P., Kleiner, A., Mobahi, H., and Neyshabur, B · 2021
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Learning an explicit hyperparameter prediction policy conditioned on tasks
Shu, J., Meng, D., and Xu, Z · 2021
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React: Out-of-distribution detection with rectified activations
Sun, Y., Guo, C., and Li, Y · 2021
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When optimizing $f$-divergence is robust with label noise
Wei, J. and Liu, Y · 2021
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Asymmetric loss functions for learning with noisy labels
Zhou, X., Liu, X., Jiang, J., Gao, X., and Ji, X · 2021
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Sample selection with uncertainty of losses for learning with noisy labels
Xia, X., Liu, T., Bo, H., Mingming, G., Jun, Y., Gang, N., and Masashi, S · 2022
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Chen, H., Shah, A., Wang, J., Tao, R., Wang, Y., Xie, X., Sugiyama, M., Singh, R., and Raj, B · 2023
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Mitigating memorization of noisy labels via regularization between representations
Cheng, H., Zhu, Z., Sun, X., and Liu, Y · 2023
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Improve noise tolerance of robust loss via noise-awareness
Ding, K., Shu, J., Meng, D., and Xu, Z · 2023
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Liu, M., Wei, J., Liu, Y., and Davis, J · 2023
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Cmw-net: Learning a class-aware sample weighting mapping for robust deep learning
Shu, J., Yuan, X., Meng, D., and Xu, Z · 2023
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To aggregate or not? learning with separate noisy labels
Wei, J., Zhu, Z., Luo, T., Amid, E., Kumar, A., and Liu, Y · 2023
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