fitting-random-labels
Example code for the paper "Understanding deep learning requires rethinking generalization" (by pluskid)
DerivativeManipulation
In the context of Deep Learning: What is the right way to conduct example weighting? How do you understand loss functions and so-called theorems on them? (by XinshaoAmosWang)
| fitting-random-labels | DerivativeManipulation | |
|---|---|---|
| 1 | 2 | |
| 177 | 10 | |
| - | - | |
| 10.0 | 10.0 | |
| almost 4 years ago | about 3 years ago | |
| Python | Shell | |
| MIT License | MIT License |
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
fitting-random-labels
Posts with mentions or reviews of fitting-random-labels.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2022-08-10.
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[Research] Deep Critical Learning (i.e., Deep Robustness) In The Era of Big Data
Code for https://arxiv.org/abs/1611.03530 found: https://github.com/pluskid/fitting-random-labels
DerivativeManipulation
Posts with mentions or reviews of DerivativeManipulation.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2022-08-10.
-
[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/1905.11233 found: https://github.com/XinshaoAmosWang/Emphasis-Regularisation-by-Gradient-Rescaling
What are some alternatives?
When comparing fitting-random-labels and DerivativeManipulation you can also consider the following projects:
IDN - AAAI 2021: Beyond Class-Conditional Assumption: A Primary Attempt to Combat Instance-Dependent Label Noise