# Robust Deep Learning via Derivative Manipulation and IMAE

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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.

## How do you think of requesting kind citations?

SIGUA: Forgetting May Make Learning with Noisy Labels More Robust

**Reason1**: Reducing a learning rate on ‘bad’ examples, is intrinsically equivalent to, reducing the weights (derivative magnitudes) of ‘bad’ data points. “SIGUA works in each mini-batch: it implements SGD on good data as usual, and if there are any bad data, it implements stochastic gradientascent (SGA) on bad data with a reduced learning rate.”

In [DM] and [IMAE], we have studied on how to model example-level weighting from the perspective of gradient/derivative. Concretely, we have claimed that those ‘bad’ examples are assigned with smaller derivative magnitude at the final layer. Mathematically, a point’s final gradient for back-propagation = its derivative * learning rate. You do not modify the derivative, instead you adjust the learning rate. But fundamentally, the principle is the same. Therefore, our work [DM] and [IMAE] should be discussed.

**Reason 2**: Although [DM] and [IMAE] are unpublished in conferences or journals, they have been released in arXiv for more than 1 year by now. Therefore, it is improper to ignore them. Furthermore, [DM] and [IMAE] are included my PhD thesis and passed the examination. PhD Thesis: Example Weighting for Deep Representation LearningI am looking forward to your ideas.

**If I am wrong, please feel free to tell me.**Otherwise, I will appreciate it significantly if you agree to discuss our work in your paper. Many thanks.