wip

thesis/bib/bibliography.bib

@@ -85,37 +85,6 @@
 	pages = {716–721},
 }
 ,
-@inproceedings{deepsvdd,
-	title = {Deep One-Class Classification},
-	author = {Ruff, Lukas and Vandermeulen, Robert and Goernitz, Nico and Deecke,
-	          Lucas and Siddiqui, Shoaib Ahmed and Binder, Alexander and M{\"u}ller
-	          , Emmanuel and Kloft, Marius},
-	booktitle = {Proceedings of the 35th International Conference on Machine
-	             Learning},
-	pages = {4393--4402},
-	year = {2018},
-	editor = {Dy, Jennifer and Krause, Andreas},
-	volume = {80},
-	series = {Proceedings of Machine Learning Research},
-	month = {10--15 Jul},
-	publisher = {PMLR},
-	pdf = {http://proceedings.mlr.press/v80/ruff18a/ruff18a.pdf},
-	url = {https://proceedings.mlr.press/v80/ruff18a.html},
-	abstract = {Despite the great advances made by deep learning in many machine
-	            learning problems, there is a relative dearth of deep learning
-	            approaches for anomaly detection. Those approaches which do exist
-	            involve networks trained to perform a task other than anomaly
-	            detection, namely generative models or compression, which are in
-	            turn adapted for use in anomaly detection; they are not trained on
-	            an anomaly detection based objective. In this paper we introduce a
-	            new anomaly detection method—Deep Support Vector Data Description—,
-	            which is trained on an anomaly detection based objective. The
-	            adaptation to the deep regime necessitates that our neural network
-	            and training procedure satisfy certain properties, which we
-	            demonstrate theoretically. We show the effectiveness of our method
-	            on MNIST and CIFAR-10 image benchmark datasets as well as on the
-	            detection of adversarial examples of GTSRB stop signs.},
-},
 @inproceedings{deep_svdd,
 	title = {Deep One-Class Classification},
 	author = {Ruff, Lukas and Vandermeulen, Robert and Goernitz, Nico and Deecke,