@article {Ardizzone2020,
	title = {Exact Information Bottleneck with Invertible Neural Networks: Getting the Best of Discriminative and Generative Modeling},
	year = {2020},
	month = {jan},
	abstract = {Generative models are more informative about underlying phenomena than discriminative ones and offer superior uncertainty quantification and out-of-distribution robustness. However, these advantages often come at the expense of reduced classification accuracy. The Information Bottleneck objective (IB) formulates this trade-off in a clean information-theoretic way, but its practical application is hampered by a lack of accurate high-dimensional estimators of mutual information (MI), its main constituent. To overcome this limitation, we develop the theory and methodology of IB-INNs, which optimize the IB objective by means of Invertible Neural Networks (INNs), without the need for approximations of MI. Our experiments show that IB-INNs allow for a precise adjustment of the generative/discriminative trade-off: They learn accurate models of the class conditional likelihoods, generalize well to unseen data and reliably detect out-of-distribution examples, while at the same time exhibiting classification accuracy close to purely discriminative feed-forward networks.},
	url = {http://arxiv.org/abs/2001.06448},
	author = {Lynton Ardizzone and Mackowiak, Radek and Carsten Rother and Ullrich K{\"o}the}
}
@proceedings {6078,
	title = {An Efficient Fusion Move Algorithm for the Minimum Cost Lifted Multicut Problem},
	volume = {LNCS 9906},
	year = {2016},
	pages = {715-730},
	publisher = {Springer},
	doi = { 10.1007/978-3-319-46475-6_44},
	author = {Thorsten Beier and Bj{\"o}rn Andres and Ullrich K{\"o}the and Fred A. Hamprecht}
}
@conference {andres_10_empirical,
	title = {An Empirical Comparison of Inference Algorithms for Graphical Models
with Higher Order Factors Using OpenGM},
	booktitle = {Pattern Recognition, Proc.~32th DAGM Symposium},
	number = {6376},
	year = {2010},
	note = {1},
	pages = {353-362},
	doi = {10.1007/978-3-642-15986-2_36},
	author = {Bj{\"o}rn Andres and J{\"o}rg H. Kappes and Ullrich K{\"o}the and Christoph Schn{\"o}rr and Fred A. Hamprecht}
}
@conference {Kappes-DAGM2010,
	title = {An Empirical Comparison of Inference Algorithms for Graphical Models with Higher Order Factors Using OpenGM},
	booktitle = {Pattern Recognition, Proc.~32th DAGM Symposium},
	year = {2010},
	author = {Bj{\"o}rn Andres and J{\"o}rg H. Kappes and Ullrich K{\"o}the and Christoph Schn{\"o}rr and Fred A. Hamprecht}
}
@article {andres_08_errors-in-variables,
	title = {On errors-in-variables regression with arbitrary covariance and its application to optical flow estimation},
	journal = {Computer Vision and Pattern Recognition, 2008. CVPR 2008. IEEE Conference on},
	year = {2008},
	note = {1},
	pages = {1-6},
	doi = {10.1109/CVPR.2008.4587571},
	author = {Bj{\"o}rn Andres and Claudia Kondermann and Daniel Kondermann and Ullrich K{\"o}the and Fred A. Hamprecht and Christoph S. Garbe}
}
