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Author Title Type [ Year(Asc)]
Blattmann, A, Milbich, T, Dorkenwald, M and Ommer, B (2021). Behavior-Driven Synthesis of Human Dynamics. CVPR2021.
Vijayan, A, Tofanelli, R, Strauss, S, Cerrone, L, Wolny, A, Strohmeier, J, Kreshuk, A, Hamprecht, F A, Smith, R S and Schneitz, K (2021). A digital 3D reference atlas reveals cellular growth patterns shaping the Arabidopsis ovule. eLife
Andersson, A, Diego, F, Hamprecht, F A and Wählby, C (2021). Istdeco: In Situ Transcriptomics Decoding By Deconvolution. bioRxiv
Afifi, M, Derpanis, K G, Ommer, B and Brown, M S (2021). Learning Multi-Scale Photo Exposure Correction. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
Walter, F C, Damrich, S and Hamprecht, F A (2021). MultiStar: Instance Segmentation of Overlapping Objects with Star-Convex Polygons . ISBI, in press
Kotovenko, D, Wright, M, Heimbrecht, A and Ommer, B (2021). Rethinking Style Transfer: From Pixels to Parameterized Brushstrokes. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
Roth, K, Milbich, T, Ommer, B, Cohen, J Paul and Ghassemi, M (2021). S2SD: Simultaneous Similarity-based Self-Distillation for Deep Metric Learning. Proceedings of International Conference on Machine Learning (ICML).
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)
Esser, P, Rombach, R and Ommer, B (2021). Taming Transformers for High-Resolution Image Synthesis. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
Damrich, S and Hamprecht, F H (2021). UMAP does not reproduce high-dimensional similarities due to negative sampling. arXiv preprint
Brattoli, B, Büchler, U, Dorkenwald, M, Reiser, P, Filli, L, Helmchen, F, Wahl, A - S and Ommer, B (2021). Unsupervised behaviour analysis and magnification (uBAM) using deep learning. Nature Machine Intelligence.
Garrido, Q, Damrich, S, Jäger, A, Cerletti, D, Claassen, M, Najman, L and Hamprecht, F A (2021). Visualizing Hierarchies In Scrna-Seq Data Using A Density Tree-Biased Autoencoder. arXiv preprint
Wolny, A, Cerrone, L, Vijayan, A, Tofanelli, R, A Barro, V, Louveaux, M, Wenzl, C, Steigleder, S, Pape, C, Bailoni, A, Duran-Nebreda, S, Bassel, G W, Lohmann, J U, Hamprecht, F A, Schneitz, K, Maizel, A and Kreshuk, A (2020). Accurate and versatile 3D segmentation of plant tissues at cellular resolution. eLife, in press
Krull, A, Hirsch, P, Rother, C, Schiffrin, A and Krull, C (2020). Artificial-intelligence-driven scanning probe microscopy. Communications Physics. 3
Schnörr, (2020). Assignment Flows. Handbook of Variational Methods for Nonlinear Geometric Data. Springer. 235—260.
Zern, A, Zeilmann, A and Schnörr, C (2020). Assignment Flows for Data Labeling on Graphs: Convergence and Stability. preprint: arXiv.
Radev, S T, Mertens, U K, Voss, A, Ardizzone, L and Köthe, U (2020). BayesFlow: Learning complex stochastic models with invertible neural networks. icon PDF (5.36 MB)
Kamann, C and Rother, C (2020). Benchmarking the Robustness of Semantic Segmentation Models. CVPR 2020. icon PDF (3.61 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. icon PDF (9.95 MB)
Lang, S and Ommer, B (2020). Das Objekt jenseits der Digitalisierung. Das digitale Objekt. 7. icon lang_ommer_digitalhumanities_2020_.pdf (599.56 KB)
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.
Bollweg, S, Haußmann, M, Kasieczka, G, Luchmann, M, Plehn, T and Thompson, J (2020). Deep-Learning Jets with Uncertainties and More. SciPost Phys. 8. icon Technical Report (1.65 MB)
Sorrenson, P, Rother, C and Köthe, U (2020). Disentanglement by Nonlinear ICA with General Incompressible-flow Networks (GIN). Intl. Conf. Learning Representations (ICLR). icon PDF (2.43 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). icon Article (13.07 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).
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. icon PDF (2.87 MB)
Zeilmann, A, Savarino, F, Petra, S and Schnörr, C (2020). Geometric Numerical Integration of the Assignment Flow. Inverse Problems. 36 034004 (33pp)
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)
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
Wolf, S (2020). Machine Learning for Instance Segmentation. Heidelberg University
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).
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
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
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)
Rombach, R, Esser, P and Ommer, B (2020). Network Fusion for Content Creation with Conditional INNs. CVPRW 2020 (AI for Content Creation).