# In deep metric learning, The improvements over time have been marginal?

Recently, in paper A Metric Learning Reality Check, it is reported that the improvements over time have been marginal at best. Is it true? I present my personal viewpoints as follows:

• First of all, acedemic research progress is naturally slow, continuous and tortuous. Beyond, it is full of flaws on its progress. For example,
• In person re-identification, several years ago, some researchers vertically split one image into several parts for alignment, which is against the design of CNNs and non-meaningful. Because deep CNNs are designed to be invariant against translation, so that hand-crafted alignment is unnecessary.

# Code Releasing of Recent Work--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.

1. IMAE for Noise-Robust Learning: Mean Absolute Error Does Not Treat Examples Equally and Gradient Magnitude’s Variance Matters

# Progressive Self Label Correction (ProSelfLC) for Training Robust Deep Neural Networks

For any specific discussion or potential future collaboration, please feel free to contact me. As a young researcher, your interest and star (citation) will mean a lot for me and my collaborators. For source codes, we are happy to provide if there is a request conditioned on academic use only and kindness to cite this work.

@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}
}


# Paper Summary on Label Manipulation, Output Regularisation (Optimisation tricks)

means being highly related to my personal research interest.

# AAAI-2020

means being highly related to my personal research interest.

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