1 code implementation • 15 Sep 2023 • Elias Ramzi, Nicolas Audebert, Clément Rambour, André Araujo, Xavier Bitot, Nicolas Thome
It provides an upperbound for rank losses and ensures robust training.
1 code implementation • 26 May 2023 • Marc Lafon, Elias Ramzi, Clément Rambour, Nicolas Thome
HEAT complements prior density estimators of the ID density, e. g. parametric models like the Gaussian Mixture Model (GMM), to provide an accurate yet robust density estimation.
1 code implementation • 5 Jul 2022 • Elias Ramzi, Nicolas Audebert, Nicolas Thome, Clément Rambour, Xavier Bitot
Image Retrieval is commonly evaluated with Average Precision (AP) or Recall@k. Yet, those metrics, are limited to binary labels and do not take into account errors' severity.
Ranked #1 on Metric Learning on DyML-Animal
1 code implementation • NeurIPS 2021 • Elias Ramzi, Nicolas Thome, Clément Rambour, Nicolas Audebert, Xavier Bitot
In image retrieval, standard evaluation metrics rely on score ranking, e. g. average precision (AP).
Ranked #2 on Image Retrieval on CUB-200-2011