Distance between Sets

By M. Levandowsky, David K. Winter. 1971

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Generalized Intersection Over Union: A Metric and a Loss for Bounding Box Regression (2019, background): “LIoU = 1− IoU , is a metric has been provided in different articles [1, 3, 4, 5].”
Deep Level Sets: Implicit Surface Representations for 3D Shape Inference (2019, methodology): “Here we considered two separate metrics for evaluating shapes inferred by our network, the Intersection over Union (IoU), also known as the Jaccard Index [19], and the Chamfer distance [1].”
A note on the triangle inequality for the Jaccard distance (2016, background): “, [15, 11, 4]), minwise independent permutations [1], or sometimes cumbersome arithmetics [10, 3].”
Learning with a Wasserstein Loss (2015, background): “The Wasserstein loss is also closely related to the Jaccard index [22], also known as intersection-over-union (IoU), which is a popular measure of performance in segmentation [23].”
The Molecular Signatures Database (MSigDB) hallmark gene set collection (2015, unknown): “For the clustering distance metric, we used the Jaccard’s distance (Jaccard, 1902; Levandowsky and Winter, 1971).”
The Tangled Web of Password Reuse (2014, unknown): “• Token-based distance functions: Dice [30], Overlap [37].”
Combating Attacks and Abuse in Large Online Communities (2016, methodology): “Inspired by the Jaccard Coefficient[135], we define the distance between sequences S1 andS2 as:”