@article {Ardizzone2019a,
	title = {Guided Image Generation with Conditional Invertible Neural Networks},
	year = {2019},
	abstract = {In this work, we address the task of natural image generation guided by a conditioning input. We introduce a new architecture called conditional invertible neural network (cINN). The cINN combines the purely generative INN model with an unconstrained feed-forward network, which efficiently preprocesses the conditioning input into useful features. All parameters of the cINN are jointly optimized with a stable, maximum likelihood-based training procedure. By construction, the cINN does not experience mode collapse and generates diverse samples, in contrast to e.g. cGANs. At the same time our model produces sharp images since no reconstruction loss is required, in contrast to e.g. VAEs. We demonstrate these properties for the tasks of MNIST digit generation and image colorization. Furthermore, we take advantage of our bi-directional cINN architecture to explore and manipulate emergent properties of the latent space, such as changing the image style in an intuitive way.},
	url = {http://arxiv.org/abs/1907.02392},
	author = {Lynton Ardizzone and Carsten L{\"u}th and Kruse, Jakob and Carsten Rother and Ullrich K{\"o}the}
}
@article {Ardizzone2019,
	title = {Guided Image Generation with Conditional Invertible Neural Networks},
	year = {2019},
	abstract = {In this work, we address the task of natural image generation guided by a conditioning input. We introduce a new architecture called conditional invertible neural network (cINN). The cINN combines the purely generative INN model with an unconstrained feed-forward network, which efficiently preprocesses the conditioning input into useful features. All parameters of the cINN are jointly optimized with a stable, maximum likelihood-based training procedure. By construction, the cINN does not experience mode collapse and generates diverse samples, in contrast to e.g. cGANs. At the same time our model produces sharp images since no reconstruction loss is required, in contrast to e.g. VAEs. We demonstrate these properties for the tasks of MNIST digit generation and image colorization. Furthermore, we take advantage of our bi-directional cINN architecture to explore and manipulate emergent properties of the latent space, such as changing the image style in an intuitive way.},
	url = {http://arxiv.org/abs/1907.02392},
	author = {Lynton Ardizzone and Carsten L{\"u}th and Kruse, Jakob and Carsten Rother and Ullrich K{\"o}the}
}
@article {schiegg_15_graphical,
	title = {Graphical Model for Joint Segmentation and Tracking of Multiple Dividing Cell},
	journal = {Bioinformatics},
	volume = {31},
	number = {6},
	year = {2015},
	note = {1},
	pages = {948-956},
	doi = {10.1093/bioinformatics/btu764},
	url = {http://bioinformatics.oxfordjournals.org/content/early/2014/11/17/bioinformatics.btu764.full.pdf?keytype=ref\&ijkey=mTXWsiFrci7R8tc},
	author = {Schiegg, M. and Hanslovsky, P. and Haubold, C. and Ullrich K{\"o}the and Hufnagel, L. and Fred A. Hamprecht}
}
@conference {andres_12_globally,
	title = {Globally Optimal Closed-Surface Segmentation for Connectomics},
	booktitle = {ECCV 2012. Proceedings, Part 3},
	number = {7574},
	year = {2012},
	note = {1},
	pages = {778-791},
	doi = {10.1007/978-3-642-33712-3_56},
	author = {Bj{\"o}rn Andres and Thorben Kr{\"o}ger and K. L. Briggmann and Denk, W. and Norogod, N. and G. W. Knott and Ullrich K{\"o}the and Fred A. Hamprecht}
}
@conference {koethe_10_geometric,
	title = {Geometric Analysis of 3D Electron Microscopy Data},
	booktitle = {Proceedings of Workshop on Discrete Geometry and Mathematical Morphology (WADGMM)},
	year = {2010},
	note = {1},
	pages = {22-26},
	author = {Ullrich K{\"o}the and Bj{\"o}rn Andres and Thorben Kr{\"o}ger and Fred A. Hamprecht},
	editor = {Ullrich K{\"o}the and Montanvert, A. and Soille, P.}
}
