@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}
}
@conference {Mackowiak2019,
	title = {CEREALS - Cost-Effective REgion-based Active Learning for Semantic Segmentation},
	booktitle = {British Machine Vision Conference 2018, BMVC 2018},
	year = {2019},
	abstract = {State of the art methods for semantic image segmentation are trained in a supervised fashion using a large corpus of fully labeled training images. However, gathering such a corpus is expensive, due to human annotation effort, in contrast to gathering unlabeled data. We propose an active learning-based strategy, called CEREALS, in which a human only has to hand-label a few, automatically selected, regions within an unlabeled image corpus. This minimizes human annotation effort while maximizing the performance of a semantic image segmentation method. The automatic selection procedure is achieved by: a) using a suitable information measure combined with an estimate about human annotation effort, which is inferred from a learned cost model, and b) exploiting the spatial coherency of an image. The performance of CEREALS is demonstrated on Cityscapes, where we are able to reduce the annotation effort to 17\%, while keeping 95\% of the mean Intersection over Union (mIoU) of a model that was trained with the fully annotated training set of Cityscapes.},
	author = {Mackowiak, Radek and Lenz, Philip and Ghori, Omair and Ferran Diego and Lange, Oliver and Carsten Rother}
}
@conference {Leistner2019,
	title = {Learning to Think Outside the Box: Wide-Baseline Light Field Depth Estimation with EPI-Shift},
	booktitle = {Proceedings - 2019 International Conference on 3D Vision, 3DV 2019},
	year = {2019},
	month = {sep},
	pages = {249{\textendash}257},
	abstract = {We propose a method for depth estimation from light field data, based on a fully convolutional neural network architecture. Our goal is to design a pipeline which achieves highly accurate results for small-and wide-baseline light fields. Since light field training data is scarce, all learning-based approaches use a small receptive field and operate on small disparity ranges. In order to work with wide-baseline light fields, we introduce the idea of EPI-Shift: To virtually shift the light field stack which enables to retain a small receptive field, independent of the disparity range. In this way, our approach {\textquoteright}learns to think outside the box of the receptive field". Our network performs joint classification of integer disparities and regression of disparity-offsets. A U-Net component provides excellent long-range smoothing. EPI-Shift considerably outperforms the state-of-the-art learning-based approaches and is on par with hand-crafted methods. We demonstrate this on a publicly available, synthetic, small-baseline benchmark and on large-baseline real-world recordings.},
	keywords = {Computer vision, deep learning, depth estimation, light fields, Stereo},
	isbn = {9781728131313},
	doi = {10.1109/3DV.2019.00036},
	url = {http://arxiv.org/abs/1909.09059 http://dx.doi.org/10.1109/3DV.2019.00036},
	author = {Titus Leistner and Schilling, Hendrik and Mackowiak, Radek and Gumhold, Stefan and Carsten Rother}
}
