Improving-Mean-Absolute-Error-against-CCE
Awesome-Learning-with-Label-Noise
| Improving-Mean-Absolute-Error-against-CCE | Awesome-Learning-with-Label-Noise | |
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| 3 | 1 | |
| 30 | 2,534 | |
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| 0.0 | 4.2 | |
| over 3 years ago | 15 days ago | |
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| MIT License | - |
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Improving-Mean-Absolute-Error-against-CCE
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[Research] Deep Critical Learning (i.e., Deep Robustness) In The Era of Big Data
Here are related papers on the fitting and generalization of deep learning: * ProSelfLC: Progressive Self Label Correction Towards A Low-Temperature Entropy State * Understanding deep learning requires rethinking generalization * A Closer Look at Memorization in Deep Networks * ProSelfLC: Progressive Self Label Correction for Training Robust Deep Neural Networks * Blog link: https://xinshaoamoswang.github.io/blogs/2020-06-07-Progressive-self-label-correction/ * Mean Absolute Error Does Not Treat Examples Equally and Gradient Magnitude’s Variance Matters * Derivative Manipulation: Example Weighting via Emphasis Density Funtion in the context of DL * Novelty: moving from loss design to derivative design
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[Research] Not all our papers get published, therefore it is enjoyable to see our released papers become a true foundation for other works
Code for https://arxiv.org/abs/1903.12141 found: https://github.com/XinshaoAmosWang/Improving-Mean-Absolute-Error-against-CCE
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[R] CVPR 2021-Progressive Self Label Correction (ProSelfLC) for Training Robust Deep Neural Networks
https://github.com/XinshaoAmosWang/Improving-Mean-Absolute-Error-against-CCE#open-reviews-and-discussion
Awesome-Learning-with-Label-Noise
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[D] Should expert opinion be a bigger part of the Machine Learning world?
And then there's learning from noisy labels. Lots of work on that as well.
What are some alternatives?
ProSelfLC-AT - noisy labels; missing labels; semi-supervised learning; entropy; uncertainty; robustness and generalisation.
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