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.
Paper link: https://arxiv.org/pdf/2005.03788.pdf

@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 Distance Metric, Representation Learning

:+1: 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

Pagination

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