DeepCriticalLearning
ProSelfLC-AT
| DeepCriticalLearning | ProSelfLC-AT | |
|---|---|---|
| 1 | 4 | |
| 31 | 58 | |
| - | - | |
| 10.0 | 1.8 | |
| over 1 year ago | almost 2 years ago | |
| Python | HTML | |
| GNU General Public License v3.0 or later | GNU General Public License v3.0 or later |
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DeepCriticalLearning
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[R] Robust Learning: the past and present. The DNN has strong fitting capability, but we find ...
Code: https://github.com/XinshaoAmosWang/DeepCriticalLearning
ProSelfLC-AT
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[R] Robust Learning: the past and present. The DNN has strong fitting capability, but we find ...
Found relevant code at https://github.com/XinshaoAmosWang/ProSelfLC-AT + all code implementations here
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[R] ProSelfLC: Progressive Self Label Correction Towards A Low-Temperature Entropy State
Code for https://arxiv.org/abs/2207.00118 found: https://github.com/XinshaoAmosWang/ProSelfLC-AT
- [P] Easy to install, use, extend, run experiments and sink results: PyTorch Implementation for ProSelfLC-CVPR 2021
- [R] CVPR 2021-Progressive Self Label Correction (ProSelfLC) for Training Robust Deep Neural Networks
What are some alternatives?
autogluon - AutoGluon: AutoML for Image, Text, Time Series, and Tabular Data [Moved to: https://github.com/autogluon/autogluon]
imodels - Interpretable ML package 🔍 for concise, transparent, and accurate predictive modeling (sklearn-compatible).
super-gradients - Easily train or fine-tune SOTA computer vision models with one open source training library. The home of Yolo-NAS.
Data-Efficient-Reinforcement-Learning-with-Probabilistic-Model-Predictive-Control - Unofficial Implementation of the paper "Data-Efficient Reinforcement Learning with Probabilistic Model Predictive Control", applied to gym environments
mmpretrain - OpenMMLab Pre-training Toolbox and Benchmark
Improving-Mean-Absolute-Error-against-CCE - Mean Absolute Error Does Not Treat Examples Equally and Gradient Magnitude’s Variance Matters
autogluon - Fast and Accurate ML in 3 Lines of Code
romodel - Modeling robust optimization problems in Pyomo
ProSelfLC - noisy labels; missing labels; semi-supervised learning; entropy; uncertainty; robustness and generalisation. [Moved to: https://github.com/XinshaoAmosWang/ProSelfLC-AT]