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.

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.

• Keywords: Label correction and smoothing, in defense of entropy minimisation, predictive uncertainty, progressive trust of a model’s knowledge.
• Github code: https://github.com/XinshaoAmosWang/ProSelfLC-CVPR2021

means being highly related to my personal research interest.

1. arXiv 2020-On the Fairness of Deep Metric Learning
2. ICCV 2019, CVPR 2020 Deep Metric Learning
3. CVPR 2019 Deep Metric Learning
4. Few-shot Learning
5. Large Output Spaces
6. Poincaré, Hyperbolic, Curvilinear
7. Wasserstein
8. Semi-supervised or Unsupervised Learning
9. NeurIPS 2019-Stochastic Shared Embeddings: Data-driven Regularization of Embedding Layers

means being highly related to my personal research interest.

1. Label Smoothing
2. Confidence Penalty
3. Label Correction
4. Example Weighting
5. Know the unknown
6. Semi-supervised learning

1. Importance sampling

1. SGD & Newton’s Method & Second-order Derivative Optimisation
2. Linear algebra
3. Probabilistic view of the world
4. Optimisation

1. Learning to Retrieve
2. Learning to Classify
3. Others

means being highly related to my personal research interest.

1. Blogs
2. Papers on Theories
3. Papers on Applications