Robust Deep Learning via Derivative Manipulation and IMAE

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 kind citation (star) will definitely mean a lot for me and my collaborators.
For any specific discussion or potential future collaboration, please feel free to contact me.

When talking about robustness/regularisation, our community tend to connnect it merely to better test performance. I advocate caring training performance as well because:

  • If noisy training examples are fitted well, a model has learned something wrong;
  • If clean ones are not fitted well, a model is not good enough.
  • There is a potential arguement that the test dataset can be infinitely large theorectically, thus being significant.
    • Personal comment: Though being true theorectically, in realistic deployment, we obtain more testing samples as time goes, accordingly we generally choose to retrain or fine-tune to make the system adaptive. Therefore, this arguement does not make much sense.

We really need to rethink robust losses and optimisation in deep learning!

  • In Normalized Loss Functions for Deep Learning with Noisy Labels, it is stated in the abstract that “we theoretically show by applying a simple normalization that: any loss can be made robust to noisy labels. However, in practice, simply being robust is not sufficient for a loss function to train accurate DNNs.
    • This statement is Quite Contradictory: A ROBUST LOSS IS NOT SUFFICIENT (i.e., ROBUST AND ACCURATE)? => Then what is value to say whether a loss is robust or not?
  • For me, a trained robust model should be accurate on both training and testing datasets.

  • I remark that we are the first to thoroughly analyse robust losses, e.g., MAE’s underfitting, and how it weights data points.
  1. IMAE for Noise-Robust Learning: Mean Absolute Error Does Not Treat Examples Equally and Gradient Magnitude’s Variance Matters
  2. Derivative Manipulation for General Example Weighting
  3. Github Pages
  4. Citation
      title={Derivative Manipulation for General Example Weighting},
      author={Wang, Xinshao and Kodirov, Elyor and Hua, Yang and Robertson, Neil M},
      journal={arXiv preprint arXiv:1905.11233},
      title={ {IMAE} for Noise-Robust Learning: Mean Absolute Error Does Not Treat Examples Equally and Gradient Magnitude's Variance Matters},
      author={Wang, Xinshao and Hua, Yang and Kodirov, Elyor and Robertson, Neil M},
      journal={arXiv preprint arXiv:1903.12141},
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