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Lukas Schott
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2020 – today
- 2024
- [i13]Leonhard Hennicke, Christian Medeiros Adriano, Holger Giese, Jan Mathias Köhler, Lukas Schott:
Mind the Gap Between Synthetic and Real: Utilizing Transfer Learning to Probe the Boundaries of Stable Diffusion Generated Data. CoRR abs/2405.03243 (2024) - [i12]Lukas Kirchdorfer, Cathrin Elich, Simon Kutsche, Heiner Stuckenschmidt, Lukas Schott, Jan Mathias Köhler:
Analytical Uncertainty-Based Loss Weighting in Multi-Task Learning. CoRR abs/2408.07985 (2024) - [i11]Niclas Popp, Dan Zhang, Jan Hendrik Metzen, Matthias Hein, Lukas Schott:
Object-Focused Data Selection for Dense Prediction Tasks. CoRR abs/2412.10032 (2024) - [i10]Advait Gadhikar, Souptik Kumar Majumdar, Niclas Popp, Piyapat Saranrittichai, Martin Rapp, Lukas Schott:
Attention Is All You Need For Mixture-of-Depths Routing. CoRR abs/2412.20875 (2024) - 2023
- [c9]Martin Bjerke, Lukas Schott, Kristopher T. Jensen, Claudia Battistin, David A. Klindt, Benjamin Adric Dunn:
Understanding Neural Coding on Latent Manifolds by Sharing Features and Dividing Ensembles. ICLR 2023 - [i9]Cathrin Elich, Lukas Kirchdorfer, Jan Mathias Köhler, Lukas Schott:
Challenging Common Assumptions in Multi-task Learning. CoRR abs/2311.04698 (2023) - 2022
- [c8]Cathrin Elich
, Lukas Kirchdorfer, Jan Mathias Köhler, Lukas Schott:
Examining Common Paradigms in Multi-task Learning. GCPR (1) 2022: 131-147 - [c7]Lukas Kirchdorfer, Cathrin Elich, Simon Kutsche, Heiner Stuckenschmidt, Lukas Schott, Jan Mathias Köhler:
Analytical Uncertainty-Based Loss Weighting in Multi-task Learning. GCPR (1) 2022: 344-361 - [c6]Lukas Schott, Julius von Kügelgen, Frederik Träuble, Peter Vincent Gehler, Chris Russell, Matthias Bethge, Bernhard Schölkopf, Francesco Locatello, Wieland Brendel:
Visual Representation Learning Does Not Generalize Strongly Within the Same Domain. ICLR 2022 - [i8]Martin Bjerke, Lukas Schott, Kristopher T. Jensen, Claudia Battistin
Understanding Neural Coding on Latent Manifolds by Sharing Features and Dividing Ensembles. CoRR abs/2210.03155 (2022) - 2021
- [b1]Lukas Schott:
Selected Inductive Biases in Neural Networks To Generalize Beyond the Training Domain. University of Tübingen, Germany, 2021 - [c5]David A. Klindt, Lukas Schott, Yash Sharma, Ivan Ustyuzhaninov, Wieland Brendel, Matthias Bethge, Dylan M. Paiton:
Towards Nonlinear Disentanglement in Natural Data with Temporal Sparse Coding. ICLR 2021 - [i7]Lukas Schott, Julius von Kügelgen, Frederik Träuble, Peter V. Gehler, Chris Russell, Matthias Bethge, Bernhard Schölkopf, Francesco Locatello, Wieland Brendel:
Visual Representation Learning Does Not Generalize Strongly Within the Same Domain. CoRR abs/2107.08221 (2021) - [i6]Roland S. Zimmermann, Lukas Schott, Yang Song, Benjamin A. Dunn, David A. Klindt:
Score-Based Generative Classifiers. CoRR abs/2110.00473 (2021) - 2020
- [c4]Evgenia Rusak, Lukas Schott, Roland S. Zimmermann, Julian Bitterwolf, Oliver Bringmann, Matthias Bethge, Wieland Brendel:
A Simple Way to Make Neural Networks Robust Against Diverse Image Corruptions. ECCV (3) 2020: 53-69 - [i5]Evgenia Rusak, Lukas Schott, Roland S. Zimmermann, Julian Bitterwolf, Oliver Bringmann, Matthias Bethge, Wieland Brendel:
Increasing the robustness of DNNs against image corruptions by playing the Game of Noise. CoRR abs/2001.06057 (2020) - [i4]David A. Klindt, Lukas Schott, Yash Sharma, Ivan Ustyuzhaninov, Wieland Brendel, Matthias Bethge, Dylan M. Paiton:
Towards Nonlinear Disentanglement in Natural Data with Temporal Sparse Coding. CoRR abs/2007.10930 (2020)
2010 – 2019
- 2019
- [c3]Lukas Schott, Jonas Rauber, Matthias Bethge, Wieland Brendel:
Towards the first adversarially robust neural network model on MNIST. ICLR (Poster) 2019 - 2018
- [i3]Lukas Schott, Jonas Rauber, Wieland Brendel, Matthias Bethge:
Robust Perception through Analysis by Synthesis. CoRR abs/1805.09190 (2018) - 2017
- [c2]Shengdong Zhang, Soheil Bahrampour, Naveen Ramakrishnan, Lukas Schott, Mohak Shah:
Deep learning on symbolic representations for large-scale heterogeneous time-series event prediction. ICASSP 2017: 5970-5974 - [c1]Steffen Wolf, Lukas Schott, Ullrich Köthe, Fred A. Hamprecht:
Learned Watershed: End-to-End Learning of Seeded Segmentation. ICCV 2017: 2030-2038 - [i2]Steffen Wolf, Lukas Schott, Ullrich Köthe, Fred A. Hamprecht:
Learned Watershed: End-to-End Learning of Seeded Segmentation. CoRR abs/1704.02249 (2017) - 2015
- [i1]Soheil Bahrampour, Naveen Ramakrishnan, Lukas Schott, Mohak Shah:
Comparative Study of Caffe, Neon, Theano, and Torch for Deep Learning. CoRR abs/1511.06435 (2015)
Coauthor Index
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