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2021

S. Damrich and Hamprecht, F. H., “UMAP does not reproduce high-dimensional similarities due to negative sampling”. arXiv preprint, 2021.

S. Damrich and Hamprecht, F. A., “On UMAP's True Loss Function”, NeurIPS. Proceedings, vol. 34. 2021.

Technical Report (1.87 MB)

M. Bellagente, Haußmann, M., Luchmann, M., and Plehn, T., “Understanding Event-Generation Networks via Uncertainties”. arXiv preprint, 2021.

A. Blattmann, Milbich, T., Dorkenwald, M., and Ommer, B., “Understanding Object Dynamics for Interactive Image-to-Video Synthesis”, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 2021.

B. Brattoli, Büchler, U., Dorkenwald, M., Reiser, P., Filli, L., Helmchen, F., Wahl, A. - S., and Ommer, B., “Unsupervised behaviour analysis and magnification (uBAM) using deep learning”, Nature Machine Intelligence, 2021.

2020

A. Wolny, Cerrone, L., Vijayan, A., Tofanelli, R., Vilches-Barro, A., Louveaux, M., Wenzel, C., Strauss, S., Wilson-Sanchez, D., Lymbouridou, R., Steigleder, S. S., Pape, C., Bailoni, A., Duran-Nebreda, S., Bassel, G. W., Lohmann, J. U., Tsiantis, M., Hamprecht, F. A., Schneitz, K., Maizel, A., and Kreshuk, A., “Accurate and Versatile 3D Segmentation of Plant Tissues at Cellular Resolution”, eLife, vol. 9, 2020.

A. Krull, Hirsch, P., Rother, C., Schiffrin, A., and Krull, C., “Artificial-intelligence-driven scanning probe microscopy”, Communications Physics, vol. 3, 2020.

C. Schnörr, “Assignment Flows”, Handbook of Variational Methods for Nonlinear Geometric Data. Springer, p. 235—260, 2020.

A. Zern, Zeilmann, A., and Schnörr, C., “Assignment Flows for Data Labeling on Graphs: Convergence and Stability”, preprint: arXiv, 2020.

S. T. Radev, Mertens, U. K., Voss, A., Ardizzone, L., and Köthe, U., “BayesFlow: Learning complex stochastic models with invertible neural networks”, 2020.

PDF (5.36 MB)

M. Haußmann, Gerwinn, S., and Kandemir, M., “Bayesian Evidential Deep Learning with PAC Regularization ”, 3rd Symposium on Advances in Approximate Bayesian Inference . 2020.

C. Kamann and Rother, C., “Benchmarking the Robustness of Semantic Segmentation Models”, in CVPR 2020, 2020.

PDF (3.61 MB)

F. Kluger, Brachmann, E., Ackermann, H., Rother, C., Yang, M. Ying, and Rosenhahn, B., “CONSAC: Robust Multi-Model Fitting by Conditional Sample Consensus”, in CVPR 2020, 2020.

PDF (9.95 MB)

S. Lang and Ommer, B., “Das Objekt jenseits der Digitalisierung”, Das digitale Objekt, vol. 7. 2020.

lang_ommer_digitalhumanities_2020_.pdf (599.56 KB)

T. Dencker, Klinkisch, P., Maul, S. M., and Ommer, B., “Deep learning of cuneiform sign detection with weak supervision using transliteration alignment”, PLoS ONE, vol. 15, no. 12, 2020.

S. Bollweg, Haußmann, M., Kasieczka, G., Luchmann, M., Plehn, T., and Thompson, J., “Deep-Learning Jets with Uncertainties and More”, SciPost Phys, vol. 8, no. 1, 2020.

Technical Report (1.65 MB)

E. Kirschbaum, Bailoni, A., and Hamprecht, F. A., “DISCo: Deep Learning, Instance Segmentation, and Correlations for Cell Segmentation in Calcium Imaging”, MICCAI. Proceedings. pp. 151-162, 2020.

P. Sorrenson, Rother, C., and Köthe, U., “Disentanglement by Nonlinear ICA with General Incompressible-flow Networks (GIN)”, in Intl. Conf. Learning Representations (ICLR), 2020.

PDF (2.43 MB)

P. Esser, Rombach, R., and Ommer, B., “A Disentangling Invertible Interpretation Network for Explaining Latent Representations”, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 2020.

Article (13.07 MB)

T. Milbich, Roth, K., Bharadhwaj, H., Sinha, S., Bengio, Y., Ommer, B., and Cohen, J. Paul, “DiVA: Diverse Visual Feature Aggregation for Deep Metric Learning”, IEEE European Conference on Computer Vision (ECCV). 2020.

T. M. Hehn, Kooij, J. F. P., and Hamprecht, F. A., “End-to-End Learning of Decision Trees and Forests”, International Journal of Computer Vision, vol. 128, pp. 997-1011, 2020.

L. Ardizzone, Mackowiak, R., Rother, C., and Köthe, U., “Exact Information Bottleneck with Invertible Neural Networks: Getting the Best of Discriminative and Generative Modeling”, 2020.

PDF (2.87 MB)

A. Zeilmann, Savarino, F., Petra, S., and Schnörr, C., “Geometric Numerical Integration of the Assignment Flow”, Inverse Problems, vol. 36, p. 034004 (33pp), 2020.

S. Wolf, Hamprecht, F. A., and Funke, J., “Inpainting Networks Learn to Separate Cells in Microscopy Images”, BMCV. 2020.

Technical Report (357.23 KB)

S. Wolf, Hamprecht, F. A., and Funke, J., “Instance Separation Emerges from Inpainting”, arXiv preprint arXiv:2003.00891, 2020.

S. Friman, “Laboratory investigations of concentration and wind profiles close to the wind-driven wavy water surface”, vol. Dissertation. Institut für Umweltphysik, Fakultät für Physik und Astronomie, Univ. Heidelberg, Heidelberg, 2020.

S. Wolf, “Machine Learning for Instance Segmentation”. Heidelberg University, 2020.

R. Rombach, Esser, P., and Ommer, B., “Making Sense of CNNs: Interpreting Deep Representations & Their Invariances with INNs”, IEEE European Conference on Computer Vision (ECCV). 2020.

H. Schilling, Gutsche, M., Brock, A., Späth, D., Rother, C., and Krispin, K., “Mind the Gap – A Benchmark for Dense Depth Prediction beyond Lidar”, in 2nd Workshop on Safe Artificial Intelligence for Automated Driving, in conjunction with CVPR 2020, 2020.

S. Wolf, Bailoni, A., Pape, C., Rahaman, N., Kreshuk, A., Köthe, U., and Hamprecht, F. A., “The Mutex Watershed and its Objective: Efficient, Parameter-Free Graph Partitioning”, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 43, pp. 3724-3738, 2020.

Technical Report (2.58 MB)

R. Rombach, Esser, P., and Ommer, B., “Network Fusion for Content Creation with Conditional INNs”, in CVPRW 2020 (AI for Content Creation), 2020.

R. Rombach, Esser, P., and Ommer, B., “Network-to-Network Translation with Conditional Invertible Neural Networks”, Neural Information Processing Systems (NeurIPS) (Oral). 2020.

P. Esser, Rombach, R., and Ommer, B., “A Note on Data Biases in Generative Models”, in NeurIPS 2020 Workshop on Machine Learning for Creativity and Design, 2020.

N. Ufer, Lang, S., and Ommer, B., “Object Retrieval and Localization in Large Art Collections Using Deep Multi-style Feature Fusion and Iterative Voting”, IEEE European Conference on Computer Vision (ECCV), VISART Workshop . 2020.

Paper (1.03 MB)

T. Milbich, Roth, K., and Ommer, B., “PADS: Policy-Adapted Sampling for Visual Similarity Learning”, in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2020, vol. 1, no. 1.

Pages