@InProceedings{BojanaGajic2022,
author="Bojana Gajic
and Ariel Amato
and Ramon Baldrich
and Joost Van de Weijer
and Carlo Gatta",
title="Area Under the ROC Curve Maximization for Metric Learning",
booktitle="CVPR 2022 Workshop on Efficien Deep Learning for Computer Vision (ECV 2022, 5th Edition)",
year="2022",
optkeywords="Training",
optkeywords="Computer vision",
optkeywords="Conferences",
optkeywords="Area measurement",
optkeywords="Benchmark testing",
optkeywords="Pattern recognition",
abstract="Most popular metric learning losses have no direct relation with the evaluation metrics that are subsequently applied to evaluate their performance. We hypothesize that training a metric learning model by maximizing the area under the ROC curve (which is a typical performance measure of recognition systems) can induce an implicit ranking suitable for retrieval problems. This hypothesis is supported by previous work that proved that a curve dominates in ROC space if and only if it dominates in Precision-Recall space. To test this hypothesis, we design and maximize an approximated, derivable relaxation of the area under the ROC curve. The proposed AUC loss achieves state-of-the-art results on two large scale retrieval benchmark datasets (Stanford Online Products and DeepFashion In-Shop). Moreover, the AUC loss achieves comparable performance to more complex, domain specific, state-of-the-art methods for vehicle re-identification.",
optnote="CIC; LAMP;",
optnote="exported from refbase (http://158.109.8.37/show.php?record=3700), last updated on Tue, 25 Apr 2023 16:00:39 +0200",
doi="10.1109/CVPRW56347.2022.00318",
file=":http://158.109.8.37/files/GAB2022.pdf:PDF"
}