Standard Dropout as Remedy for Training Deep Neural Networks with Label Noise

[1]  Richard Nock,et al.  Making Deep Neural Networks Robust to Label Noise: A Loss Correction Approach , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[2]  Nitish Srivastava,et al.  Dropout: a simple way to prevent neural networks from overfitting , 2014, J. Mach. Learn. Res..

[3]  Yoshua Bengio,et al.  Why Does Unsupervised Pre-training Help Deep Learning? , 2010, AISTATS.

[4]  Arash Vahdat,et al.  Toward Robustness against Label Noise in Training Deep Discriminative Neural Networks , 2017, NIPS.

[5]  Nagarajan Natarajan,et al.  Learning with Noisy Labels , 2013, NIPS.

[6]  Nitish Srivastava,et al.  Improving neural networks by preventing co-adaptation of feature detectors , 2012, ArXiv.

[7]  Geoffrey E. Hinton,et al.  Who Said What: Modeling Individual Labelers Improves Classification , 2017, AAAI.

[8]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[9]  Yoshua Bengio,et al.  A Closer Look at Memorization in Deep Networks , 2017, ICML.

[10]  Andrzej Rusiecki,et al.  Robust learning algorithm based on LTA estimator , 2013, Neurocomputing.

[11]  Allan Jabri,et al.  Learning Visual Features from Large Weakly Supervised Data , 2015, ECCV.

[12]  Xiaogang Wang,et al.  Learning from massive noisy labeled data for image classification , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[13]  Geoffrey E. Hinton,et al.  Semantic hashing , 2009, Int. J. Approx. Reason..

[14]  Andrzej Rusiecki,et al.  Reducing noise impact on MLP training , 2016, Soft Comput..

[15]  A. Rusiecki.  Trimmed categorical cross‐entropy for deep learning with label noise , 2019, Electronics Letters.

[16]  Yoshua Bengio,et al.  Greedy Layer-Wise Training of Deep Networks , 2006, NIPS.

[17]  Shahrokh Valaee,et al.  Survey of Dropout Methods for Deep Neural Networks , 2019, ArXiv.

[18]  Abhinav Gupta,et al.  Learning from Noisy Large-Scale Datasets with Minimal Supervision , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[19]  Ross B. Girshick,et al.  Seeing through the Human Reporting Bias: Visual Classifiers from Noisy Human-Centric Labels , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[20]  Pietro Perona,et al.  Building a bird recognition app and large scale dataset with citizen scientists: The fine print in fine-grained dataset collection , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[21]  Matthew S. Nokleby,et al.  Learning Deep Networks from Noisy Labels with Dropout Regularization , 2016, 2016 IEEE 16th International Conference on Data Mining (ICDM).