From 838968352a3bd7af65ad7d00af3653b8168af370 Mon Sep 17 00:00:00 2001 From: "J.B" Date: Mon, 3 Apr 2023 21:31:58 +0200 Subject: [PATCH] updated recommended citations with new sources --- readme.md | 20 +++++++++++++------- 1 file changed, 13 insertions(+), 7 deletions(-) diff --git a/readme.md b/readme.md index 4f7c87c..35875e4 100644 --- a/readme.md +++ b/readme.md @@ -78,19 +78,25 @@ Example methods for examining results of such evaluations are shown in `analyze_ When using **NINCO**, please consider citing (besides this paper) the following data sources that were used to create NINCO: ``` -Hendrycks et al.: "Scaling out-of-distribution detection for real-world settings", ICML, 2022. -Bossard et al.: Food-101 – mining discriminative components with random forests", ECCV 2014. -Zhou et al.: "Places: A 10 million image database for scene recognition", IEEE PAMI 2017. -Huang et al.: "Mos: Towards scaling out-of-distribution detection for large semantic space", CVPR 2021. +Hendrycks et al.: ”Scaling out-of-distribution detection for real-world settings”, ICML, 2022. +Bossard et al.: ”Food-101 – mining discriminative components with random forests”, ECCV 2014. +Zhou et al.: ”Places: A 10 million image database for scene recognition”, IEEE PAMI 2017. +Huang et al.: ”Mos: Towards scaling out-of-distribution detection for large semantic space”, CVPR 2021. +Li et al.: ”Caltech 101 (1.0)”, 2022. +Ismail et al.: ”MYNursingHome: A fully-labelled image dataset for indoor object classification.”, Data in Brief (V. 32) 2020. The iNaturalist project: https://www.inaturalist.org/ ``` When using **NINCO_popular_datasets_subsamples**, additionally to the above, please consider citing: ``` -Cimpoi et al.: "Describing textures in the wild", CVPR 2014. -Hendrycks et al.: "Natural adversarial examples", CVPR 2021. -Wang et al.: "Vim: Out-of-distribution with virtual-logit matching", CVPR 2022. +Cimpoi et al.: ”Describing textures in the wild”, CVPR 2014. +Hendrycks et al.: ”Natural adversarial examples”, CVPR 2021. +Wang et al.: ”Vim: Out-of-distribution with virtual-logit matching”, CVPR 2022. +Bendale et al.: ”Towards Open Set Deep Networks”, CVPR 2016. +Vaze et al.: ”Open-set Recognition: a Good Closed-set Classifier is All You Need?”, ICLR 2022. +Wang et al.: ”Partial and Asymmetric Contrastive Learning for Out-of-Distribution Detection in Long-Tailed Recognition.” ICML, 2022. +Galil et al.: “A framework for benchmarking Class-out-of-distribution detection and its application to ImageNet”, ICLR 2023. ``` For citing our paper, we would appreciate using the following bibtex entry: