All Publications

2021

Islam, M Amirul, Kowal, M, Esser, P, Jia, S, Ommer, B, Derpanis, K G and Bruce, N (2021). Shape or Texture: Understanding Discriminative Features in CNNs. International Conference on Learning Representations (ICLR)

2020

Roth, K, Milbich, T, Sinha, S, Gupta, P, Ommer, B and Cohen, J Paul (2020). Revisiting Training Strategies and Generalization Performance in Deep Metric Learning. International Conference on Machine Learning (ICML). https://arxiv.org/pdf/2002.08473.pdf
Desana, M and Schnörr, C (2020). Sum-Product Graphical Models. Machine Learning. 109 135–173
Zeilmann, A, Savarino, F, Petra, S and Schnörr, C (2020). Geometric Numerical Integration of the Assignment Flow. Inverse Problems. 36 034004 (33pp)
Zern, A, Zeilmann, A and Schnörr, C (2020). Assignment Flows for Data Labeling on Graphs: Convergence and Stability. preprint: arXiv. https://arxiv.org/abs/2002.11571
Zern, A, Zisler, M, Petra, S and Schnörr, C (2020). Unsupervised Assignment Flow: Label Learning on Feature Manifolds by Spatially Regularized Geometric Assignment. Journal of Mathematical Imaging and Vision. https://doi.org/10.1007/s10851-019-00935-7
Censor, Y, Petra, S and Schnörr, C (2020). Superiorization vs. Accelerated Convex Optimization: The Superiorized/Regularized Least Squares Case. J. Appl. Numer. Optimization (in press; arXiv:1911.05498). 2 15-62. http://jano.biemdas.com/archives/1060
Schnörr, (2020). Assignment Flows. Handbook of Variational Methods for Nonlinear Geometric Data. Springer. 235—260. https://www.springer.com/gp/book/9783030313500
Ardizzone, L, Mackowiak, R, Rother, C and Köthe, U (2020). Exact Information Bottleneck with Invertible Neural Networks: Getting the Best of Discriminative and Generative Modeling. http://arxiv.org/abs/2001.06448PDF icon PDF (2.87 MB)
Haller, S, Prakash, M, Hutschenreiter, L, Pietzsch, T, Rother, C, Jug, F, Swoboda, P and Savchynskyy, B (2020). A Primal-Dual Solver for Large-Scale Tracking-by-Assignment. AISTATS 2020PDF icon PDF (1.04 MB)
Schilling, H, Gutsche, M, Brock, A, Späth, D, Rother, C and Krispin, K (2020). Mind the Gap – A Benchmark for Dense Depth Prediction beyond Lidar. 2nd Workshop on Safe Artificial Intelligence for Automated Driving, in conjunction with CVPR 2020
Sorrenson, P, Rother, C and Köthe, U (2020). Disentanglement by Nonlinear ICA with General Incompressible-flow Networks (GIN). Intl. Conf. Learning Representations (ICLR). http://arxiv.org/abs/2001.04872PDF icon PDF (2.43 MB)
Radev, S T, Mertens, U K, Voss, A, Ardizzone, L and Köthe, U (2020). BayesFlow: Learning complex stochastic models with invertible neural networks. http://arxiv.org/abs/2003.06281PDF icon PDF (5.36 MB)
Bhowmik, A, Gumhold, S, Rother, C and Brachmann, E (2020). Reinforced Feature Points: Optimizing Feature Detection and Description for a High-Level Task. CVPR 2020 (oral). http://arxiv.org/abs/1912.00623PDF icon PDF (2.74 MB)
Kamann, C and Rother, C (2020). Benchmarking the Robustness of Semantic Segmentation Models. CVPR 2020. http://arxiv.org/abs/1908.05005PDF icon PDF (3.61 MB)
Krull, A, Hirsch, P, Rother, C, Schiffrin, A and Krull, C (2020). Artificial-intelligence-driven scanning probe microscopy. Communications Physics. 3
Wolf, S, Bailoni, A, Pape, C, Rahaman, N, Kreshuk, A, Köthe, U and Hamprecht, F A (2020). The Mutex Watershed and its Objective: Efficient, Parameter-Free Graph Partitioning. IEEE Transactions on Pattern Analysis and Machine IntelligencePDF icon Technical Report (2.58 MB)
Esser, P, Rombach, R and Ommer, B (2020). A Disentangling Invertible Interpretation Network for Explaining Latent Representations. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). https://compvis.github.io/iin/PDF icon Article (13.07 MB)
Kluger, F, Brachmann, E, Ackermann, H, Rother, C, Yang, M Ying and Rosenhahn, B (2020). CONSAC: Robust Multi-Model Fitting by Conditional Sample Consensus. CVPR 2020. http://arxiv.org/abs/2001.02643PDF icon PDF (9.95 MB)
Milbich, T, Roth, K, Bharadhwaj, H, Sinha, S, Bengio, Y, Ommer, B and Cohen, J Paul (2020). DiVA: Diverse Visual Feature Aggregation for Deep Metric Learning. IEEE European Conference on Computer Vision (ECCV). https://arxiv.org/abs/2004.13458
Wolf, S (2020). Machine Learning for Instance Segmentation. Heidelberg University
Tourani, S, Shekhovtsov, A, Rother, C and Savchynskyy, B (2020). Taxonomy of Dual Block-Coordinate Ascent Methods for Discrete Energy Minimization. AISTATS 2020. https://gitlab.com/PDF icon PDF (2.58 MB)
Jähne, (2020). What controls air-sea gas exchange at extreme wind speeds? Evidence from laboratory experiments. Recent Advances in the Study of Oceanic Whitecaps. Springer. 133–150
Friman, S (2020). Laboratory investigations of concentration and wind profiles close to the wind-driven wavy water surface. Institut für Umweltphysik, Fakultät für Physik und Astronomie, Univ. Heidelberg, Heidelberg. Dissertation
Mustikovela, S K, Jampani, V, De Mello, S, Liu, S, Iqbal, U, Rother, C and Kautz, J (2020). Self-Supervised Viewpoint Learning From Image Collections. CONSAC. https://github.com/NVlabs/SSVPDF icon PDF (8.77 MB)
Milbich, T, Roth, K, Brattoli, B and Ommer, B (2020). Sharing Matters for Generalization in Deep Metric Learning. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI). https://arxiv.org/abs/2004.05582
Milbich, T, Roth, K and Ommer, B (2020). PADS: Policy-Adapted Sampling for Visual Similarity Learning. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 1. https://arxiv.org/abs/2003.11113
Milbich, T, Ghori, O and Ommer, B (2020). Unsupervised Representation Learning by Discovering Reliable Image Relations. Pattern Recognition. 102. http://arxiv.org/abs/1911.07808
Afifi, M, Derpanis, K G, Ommer, B and Brown, M S (2020). Learning to Correct Overexposed and Underexposed Photos. https://arxiv.org/abs/2003.11596
Dencker, T, Klinkisch, P, Maul, S M and Ommer, B (2020). Deep learning of cuneiform sign detection with weak supervision using transliteration alignment. PLoS ONE. 15. https://doi.org/10.1371/journal.pone.0243039
Esser, P, Rombach, R and Ommer, B (2020). Taming Transformers for High-Resolution Image Synthesis. https://arxiv.org/abs/2012.09841
Esser, P, Rombach, R and Ommer, B (2020). A Note on Data Biases in Generative Models. NeurIPS 2020 Workshop on Machine Learning for Creativity and Design. https://arxiv.org/abs/2012.02516
Pape, C, Remme, R, Wolny, A, Olberg, S, Wolf, S, Cerrone, L, Cortese, M, Klaus, S, Lucic, B, Ullrich, S, Anders-Össwein, M, Wolf, S, Cerikan, B, Neufeldt, C J, Ganter, M, Schnitzler, P, Merle, U, Lusic, M, Boulant, S, Stanifer, M, Bartenschlager, R, Hamprecht, F A, Kreshuk, A, Tischer, C, Kräusslich, H - G, Müller, B and Laketa, V (2020). Microscopy-based assay for semi-quantitative detection of SARS-CoV-2 specific antibodies in human sera. BioEssays, in press
Rombach, R, Esser, P and Ommer, B (2020). Network Fusion for Content Creation with Conditional INNs. CVPRW 2020 (AI for Content Creation). https://compvis.github.io/network-fusion/
Wolf, S, Li, Y, Pape, C, Bailoni, A, Kreshuk, A and Hamprecht, F A (2020). The Semantic Mutex Watershed for Efficient Bottom-Up Semantic Instance Segmentation. ECCV. Proceedings. 208-224
Rombach, R, Esser, P and Ommer, B (2020). Network-to-Network Translation with Conditional Invertible Neural Networks. Neural Information Processing Systems (NeurIPS) (Oral). https://compvis.github.io/net2net/
Braun, S, Esser, P and Ommer, B (2020). Unsupervised Part Discovery by Unsupervised Disentanglement. Proceedings of the German Conference on Pattern Recognition (GCPR) (Oral). Tübingen. https://compvis.github.io/unsupervised-part-segmentation/
Wolf, S, Hamprecht, F A and Funke, J (2020). Inpainting Networks Learn to Separate Cells in Microscopy Images. BMCV, in pressPDF icon Technical Report (357.23 KB)
Rombach, R, Esser, P and Ommer, B (2020). Making Sense of CNNs: Interpreting Deep Representations & Their Invariances with INNs. IEEE European Conference on Computer Vision (ECCV). https://compvis.github.io/invariances/
Bailoni, A, Pape, C, Wolf, S, Kreshuk, A and Hamprecht, F A (2020). Proposal-Free Volumetric Instance Segmentation from Latent Single-Instance Masks. GCPR, accepted. arXiv

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