Fetching the paper…
Reading the bibliography…
To discover intrinsic inter-class transition probabilities underlying data, learning with noise transition has become an important approach for robust deep learning on corrupted labels.
Probability in Banach Spaces: Isoperimetry and Processes , volume 23
Ledoux, M. and Talagrand, M · 1991
Earlier work this paper cites.
Learning to control fast-weight memories: An alternative to dynamic recurrent networks
Schmidhuber, J · 1992
Earlier work this paper cites.
Learning to learn
Thrun, S. and Pratt, L · 1998
Earlier work this paper cites.
Rademacher and gaussian complexities: Risk bounds and structural results
Bartlett, P. L. and Mendelson, S · 2002
Earlier work this paper cites.
Convexity, classification, and risk bounds
Bartlett, P. L., Jordan, M. I., and McAuliffe, J. D · 2006
Earlier work this paper cites.
Learning multiple layers of features from tiny images
Krizhevsky, A · 2009
Earlier work this paper cites.
Self-paced learning for latent variable models
Kumar, M. P., Packer, B., and Koller, D · 2010
Earlier work this paper cites.
Deep sparse rectifier neural networks
Glorot, X., Bordes, A., and Bengio, Y · 2011
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.
Facial age estimation by learning from label distributions
Geng, X., Yin, C., and Zhou, Z.-H · 2013
Earlier work this paper cites.
Learning with noisy labels
Natarajan, N., Dhillon, I. S., Ravikumar, P. K., and Tewari, A · 2013
Earlier work this paper cites.
Classification with asymmetric label noise: Consistency and maximal denoising
Scott, C., Blanchard, G., and Handy, G · 2013
Earlier work this paper cites.
Learning to predict from crowdsourced data
Bi, W., Wang, L., Kwok, J. T., and Tu, Z · 2014
Earlier work this paper cites.
Distilling the knowledge in a neural network
Hinton, G., Vinyals, O., and Dean, J · 2015
Earlier work this paper cites.
A rate of convergence for mixture proportion estimation, with application to learning from noisy labels
Scott, C · 2015
Earlier work this paper cites.
Training convolutional networks with noisy labels
Sukhbaatar, S., Bruna, J., Paluri, M., Bourdev, L., and Fergus, R · 2015
Earlier work this paper cites.
Learning from massive noisy labeled data for image classification
Xiao, T., Xia, T., Yang, Y., Huang, C., and Wang, X · 2015
Earlier work this paper cites.
Label distribution learning
Geng, X · 2016
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 networks from noisy labels with dropout regularization
Jindal, I., Nokleby, M., and Chen, X · 2016
Earlier work this paper cites.
Adversarial examples in the physical world
Kurakin, A., Goodfellow, I., and Bengio, S · 2016
Cited alongside, same era.
Learning to detect concepts from webly-labeled video data
Liang, J., Jiang, L., Meng, D., and Hauptmann, A. G · 2016
Cited alongside, same era.
Rethinking the inception architecture for computer vision
Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., and Wojna, Z · 2016
Cited alongside, same era.
Spectrally-normalized margin bounds for neural networks
Bartlett, P. L., Foster, D. J., and Telgarsky, M. J · 2017
Cited alongside, same era.
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.
Model-agnostic meta-learning for fast adaptation of deep networks
Finn, C., Abbeel, P., and Levine, S · 2017
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
Later among the works it cites.
Towards deep learning models resistant to adversarial attacks
Madry, A., Makelov, A., Schmidt, L., Tsipras, D., and Vladu, A · 2018
Later among the works it cites.
Foundations of Machine Learning
Mohri, M., Rostamizadeh, A., and Talwalkar, A · 2018
Later among the works it cites.
A pac-bayesian approach to spectrally-normalized margin bounds for neural networks
Neyshabur, B., Bhojanapalli, S., and Srebro, N · 2018
Later among the works it cites.
Do cifar-10 classifiers generalize to cifar-10?
Recht, B., Roelofs, R., Schmidt, L., and Shankar, V · 2018
Later among the works it cites.
Learning to reweight examples for robust deep learning
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Training deep neural-networks using a noise adaptation layer
Goldberger, J. and Ben-Reuven, E · 2017
Cited alongside, same era.
Learning from noisy labels with distillation
Li, Y., Yang, J., Song, Y., Cao, L., Luo, J., and Li, L.-J · 2017
Cited alongside, same era.
Decoupling" when to update" from" how to update"
Malach, E. and Shalev-Shwartz, S · 2017
Cited alongside, same era.
A theoretical understanding of self-paced learning
Meng, D., Zhao, Q., and Jiang, L · 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
Cited alongside, same era.
Toward robustness against label noise in training deep discriminative neural networks
Vahdat, A · 2017
Cited alongside, same era.
Ren, M., Zeng, W., Yang, B., and Urtasun, R · 2018
Later among the works it cites.
Small sample learning in big data era
Shu, J., Xu, Z., and Meng, D · 2018
Later among the works it cites.
Joint optimization framework for learning with noisy labels
Tanaka, D., Ikami, D., Yamasaki, T., and Aizawa, K · 2018
Later among the works it cites.
Unsupervised label noise modeling and loss correction
Arazo, E., Ortego, D., Albert, P., O’Connor, N. E., and McGuinness, K · 2019
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
Later among the works it cites.
Pytorch: An imperative style, high-performance deep learning library
Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al · 2019
Later among the works it cites.
Human uncertainty makes classification more robust
Peterson, J. C., Battleday, R. M., Griffiths, T. L., and Russakovsky, O · 2019
Later among the works it cites.
Learning with bad training data via iterative trimmed loss minimization
Shen, Y. and Sanghavi, S · 2019
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.
Selfie: Refurbishing unclean samples for robust deep learning
Song, H., Kim, M., and Lee, J.-G · 2019
Later among the works it cites.
Are anchor points really indispensable in label-noise learning?
Xia, X., Liu, T., Wang, N., Han, B., Gong, C., Niu, G., and Sugiyama, M · 2019
Later among the works it cites.
Safeguarded dynamic label regression for noisy supervision
Yao, J., Wu, H., Zhang, Y., Tsang, I. W., and Sun, J · 2019
Later among the works it cites.
Rademacher complexity for adversarially robust generalization
Yin, D., Kannan, R., and Bartlett, P · 2019
Later among the works it cites.
How does disagreement help generalization against label corruption?
Yu, X., Han, B., Yao, J., Niu, G., Tsang, I., and Sugiyama, M · 2019
Later among the works it cites.
Generalized cross entropy loss for training deep neural networks with noisy labels
Zhang, Z. and Sabuncu, M · 2019
Later among the works it cites.