Joint Optimization Framework for Learning with Noisy Labels

Semi-supervised learning literature survey

Original

X. Zhu · 2006

Earlier work this paper cites.

k-means++: The advantages of careful seeding

D. Arthur and S. Vassilvitskii · 2007

Earlier work this paper cites.

Analysis of semi-supervised learning with the yarowsky algorithm

G. R. Haffari and A. Sarkar · 2007

Earlier work this paper cites.

Learning multiple layers of features from tiny images

A. Krizhevsky and G. Hinton · 2009

Earlier work this paper cites.

Support vector machines with the ramp loss and the hard margin loss

J. P. Brooks · 2011

Earlier work this paper cites.

Pseudo-label: The simple and efficient semi-supervised learning method for deep neural networks

D.-H. Lee · 2013

Earlier work this paper cites.

Making risk minimization tolerant to label noise

A. Ghosh, N. Manwani, and P. Sastry · 2015

Earlier work this paper cites.

Explaining and harnessing adversarial examples

I. J. Goodfellow, J. Shlens, and C. Szegedy · 2015

Earlier work this paper cites.

Training deep neural networks on noisy labels with bootstrapping

S. Reed, H. Lee, D. Anguelov, C. Szegedy, D. Erhan, and A. Rabinovich · 2015

Cited alongside, same era.

Training convolutional networks with noisy labels

S. Sukhbaatar, J. Bruna, M. Paluri, L. Bourdev, and R. Fergus · 2015

Cited alongside, same era.

Chainer: a next-generation open source framework for deep learning

S. Tokui, K. Oono, S. Hido, and J. Clayton · 2015

Cited alongside, same era.

Learning with symmetric label noise: The importance of being unhinged

B. Van Rooyen, A. Menon, and R. C. Williamson · 2015

Cited alongside, same era.

Learning from massive noisy labeled data for image classification

T. Xiao, T. Xia, Y. Yang, C. Huang, and X. Wang · 2015

Cited alongside, same era.

Deep residual learning for image recognition

K. He, X. Zhang, S. Ren, and J. Sun · 2016

Cited alongside, same era.

The new data and new challenges in multimedia research

B. Thomee, D. A. Shamma, G. Friedland, B. Elizalde, K. Ni, D. Poland, D. Borth, and L.-J. Li · 2016

Later among the works it cites.

A closer look at memorization in deep networks

D. Arpit, S. Jastrzębski, N. Ballas, D. Krueger, E. Bengio, M. S. Kanwal, T. Maharaj, A. Fischer, A. Courville, Y. Bengio, et al · 2017

Later among the works it cites.

Robust loss functions under label noise for deep neural networks

A. Ghosh, H. Kumar, and P. Sastry · 2017

Later among the works it cites.

Learning discrete representations via information maximizing self augmented training

W. Hu, T. Miyato, S. Tokui, E. Matsumoto, and M. Sugiyama · 2017

Later among the works it cites.

Making neural networks robust to label noise: a loss correction approach

G. Patrini, A. Rozza, A. Menon, R. Nock, and L. Qu · 2017

Later among the works it cites.

Toward robustness against label noise in training deep discriminative neural networks