Code Releasing of Recent Work--Derivative Manipulation and IMAE

18 Feb 2020 in Blogs

For source codes, the usage is conditioned on academic use only and kindness to cite our work: Derivative Manipulation and IMAE.
As a young researcher, your interest and star (citation) will mean a lot for me and my collaborators.
For any specific discussion or potential future collaboration, please feel free to contact me.

1. IMAE for Noise-Robust Learning: Mean Absolute Error Does Not Treat Examples Equally and Gradient Magnitude’s Variance Matters
   • Following work: Derivative Manipulation for General Example Weighting
2. Derivative Manipulation for General Example Weighting
   • Preliminary: IMAE for Noise-Robust Learning: Mean Absolute Error Does Not Treat Examples Equally and Gradient Magnitude’s Variance Matters
3. Github Pages
   • DerivativeManipulation
   • IMAE
4. Citation

   @article{wang2020proselflc,
     title={ProSelfLC: Progressive Self Label Correction 
     for Training Robust Deep Neural Networks},
     author={Wang, Xinshao and Hua, Yang and Kodirov, Elyor and Robertson, Neil M},
     journal={arXiv preprint arXiv:2005.03788},
     year={2020}
   }
   

   @article{wang2020proselflc,
     title={ProSelfLC: Progressive Self Label Correction 
     for Training Robust Deep Neural Networks},
     author={Wang, Xinshao and Hua, Yang and Kodirov, Elyor and Robertson, Neil M},
     journal={arXiv preprint arXiv:2005.03788},
     year={2020}
   }