updated recommended citations with new sources

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: