Fetching the paper…
Reading the bibliography…
Noisy labels are very common in real-world training data, which lead to poor generalization on test data because of overfitting to the noisy labels.
Combining labeled and unlabeled data with co-training
Blum, A. and Mitchell, T · 1998
Earlier work this paper cites.
LOF: Identifying density-based local outliers
Breunig, M. M., Kriegel, H.-P., Ng, R. T., and Sander, J · 2000
Earlier work this paper cites.
Learning from noisy labels with deep neural networks: A survey
Song, H., Kim, M., Park, D., and Lee, J.-G · 2007
Earlier work this paper cites.
ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., and Hinton, G. E · 2012
Earlier work this paper cites.
Food-101–mining discriminative components with random forests
Bossard, L., Guillaumin, M., and Van Gool, L · 2014
Earlier work this paper cites.
CIFAR-10 and CIFAR-100 datasets, 2014
Krizhevsky, A., Nair, V., and Hinton, G · 2014
Earlier work this paper cites.
Training deep neural networks on noisy labels with bootstrapping
Reed, S., Lee, H., Anguelov, D., Szegedy, C., Erhan, D., and Rabinovich, A · 2015
Earlier work this paper cites.
You only look once: Unified, real-time object detection
Redmon, J., Divvala, S., Girshick, R., and Farhadi, A · 2016
Earlier work this paper cites.
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
Earlier work this paper cites.
Active Bias: Training more accurate neural networks by emphasizing high variance samples
Chang, H.-S., Learned-Miller, E., and McCallum, A · 2017
Cited alongside, same era.
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., Menon, A. K., Nock, R., and Qu, L · 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.
Understanding deep learning requires rethinking generalization
Zhang, C., Bengio, S., Hardt, M., Recht, B., and Vinyals, O · 2017
Cited alongside, same era.
Learning to reweight examples for robust deep learning
Ren, M., Zeng, W., Yang, B., and Urtasun, R · 2018
Later among the works it cites.
Iterative learning with open-set noisy labels
Wang, Y., Liu, W., Ma, X., Bailey, J., Zha, H., Song, L., and Xia, S.-T · 2018
Later among the works it cites.
Understanding and utilizing deep neural networks trained with noisy labels
Chen, P., Liao, B. B., Chen, G., and Zhang, S · 2019
Closest in time.
Using pre-training can improve model robustness and uncertainty
Hendrycks, D., Lee, K., and Mazeika, M · 2019
Closest in time.
Li, M., Soltanolkotabi, M., and Oymak, S · 2019
Closest in time.
Generalization guarantees for neural networks via harnessing the low-rank structure of the jacobian
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Han, B., Yao, Q., Yu, X., Niu, G., Xu, M., Hu, W., Tsang, I., and Sugiyama, M · 2018
Cited alongside, same era.
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
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 · 2018
Cited alongside, same era.
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
Cited alongside, same era.
Carpe diem, seize the samples uncertain “at the moment” for adaptive batch selection
Song, H., Kim, M., Kim, S., and Lee, J.-G
Cited in the paper.
Ada-boundary: accelerating dnn training via adaptive boundary batch selection
Song, H., Kim, S., Kim, M., and Lee, J.-G
Cited in the paper.
Oymak, S., Fabian, Z., Li, M., and Soltanolkotabi, M · 2019
Closest in time.
Learning with bad training data via iterative trimmed loss minimization
Shen, Y. and Sanghavi, S · 2019
Closest in time.
SELFIE: Refurbishing unclean samples for robust deep learning
Song, H., Kim, M., and Lee, J.-G · 2019
Closest in time.
How does disagreement help generalization against label corruption?
Yu, X., Han, B., Yao, J., Niu, G., Tsang, I., and Sugiyama, M · 2019
Closest in time.