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Evgenia Rusak
Evgenia Rusak
M.Sc.
Verified email at uni-tuebingen.de
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Cited by
Year
Active tuning of all-dielectric metasurfaces
J Sautter, I Staude, M Decker, E Rusak, DN Neshev, I Brener, YS Kivshar
ACS nano 9 (4), 4308-4315, 2015
3892015
Benchmarking robustness in object detection: Autonomous driving when winter is coming
C Michaelis, B Mitzkus, R Geirhos, E Rusak, O Bringmann, AS Ecker, ...
arXiv preprint arXiv:1907.07484, 2019
2612019
Improving robustness against common corruptions by covariate shift adaptation
S Schneider, E Rusak, L Eck, O Bringmann, W Brendel, M Bethge
Advances in Neural Information Processing Systems 33, 11539-11551, 2020
2282020
A simple way to make neural networks robust against diverse image corruptions
E Rusak, L Schott, RS Zimmermann, J Bitterwolf, O Bringmann, M Bethge, ...
Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23 …, 2020
1422020
Hybrid nanoantennas for directional emission enhancement
E Rusak, I Staude, M Decker, J Sautter, AE Miroshnichenko, DA Powell, ...
Applied Physics Letters 105 (22), 221109, 2014
952014
Manipulation of photoluminescence of two-dimensional MoSe2 by gold nanoantennas
H Chen, J Yang, E Rusak, J Straubel, R Guo, YW Myint, J Pei, M Decker, ...
Scientific reports 6 (1), 1-11, 2016
882016
Multipolar coupling in hybrid metal–dielectric metasurfaces
R Guo, E Rusak, I Staude, J Dominguez, M Decker, C Rockstuhl, I Brener, ...
Acs Photonics 3 (3), 349-353, 2016
822016
Manipulation of Magnetic Dipole Emission from Eu3+ with Mie-Resonant Dielectric Metasurfaces
A Vaskin, S Mashhadi, M Steinert, KE Chong, D Keene, S Nanz, A Abass, ...
Nano letters 19 (2), 1015-1022, 2019
772019
INCREASING THE ROBUSTNESS OF DNNS AGAINST IM-AGE CORRUPTIONS BY PLAYING THE GAME OF NOISE
E Rusak, L Schott, R Zimmermann, J Bitterwolf, O Bringmann, M Bethge, ...
412020
Enhancement of and interference among higher order multipole transitions in molecules near a plasmonic nanoantenna
E Rusak, J Straubel, P Gładysz, M Göddel, A Kędziorski, M Kühn, ...
Nature Communications 10 (1), 5775, 2019
202019
Adapting imagenet-scale models to complex distribution shifts with self-learning
E Rusak, S Schneider, P Gehler, O Bringmann, W Brendel, M Bethge
arXiv preprint arXiv:2104.12928, 2021
102021
Formation of nanocrystalline graphene on germanium
R Yekani, E Rusak, A Riaz, A Felten, B Breitung, S Dehm, D Perera, ...
Nanoscale 10 (25), 12156-12162, 2018
92018
Benchmarking robustness in object detection: Autonomous driving when winter is coming. arXiv 2019
C Michaelis, B Mitzkus, R Geirhos, E Rusak, O Bringmann, AS Ecker, ...
arXiv preprint arXiv:1907.07484, 1907
91907
Robust deep learning object recognition models rely on low frequency information in natural images
Z Li, J Ortega Caro, E Rusak, W Brendel, M Bethge, F Anselmi, AB Patel, ...
PLOS Computational Biology 19 (3), e1010932, 2023
72023
If your data distribution shifts, use self-learning
E Rusak, S Schneider, G Pachitariu, L Eck, PV Gehler, O Bringmann, ...
Transactions on Machine Learning Research, 2021
72021
Benchmarking Robustness in Object Detection: Autonomous Driving When Winter Is Coming. 2020
C Michaelis, B Mitzkus, R Geirhos, E Rusak, O Bringmann, AS Ecker, ...
arXiv preprint arXiv:1907.07484, 1907
71907
ACS Nano 2015, 9, 4308–4315 DOI: 10.1021/acsnano. 5b00723 [ACS Full Text ACS Full Text]
J Sautter, I Staude, M Decker, E Rusak, DN Neshev, I Brener, YS Kivshar
Google Scholar There is no corresponding record for this reference, 0
6
ACS Nano 9, 4308 (2015)
J Sautter, I Staude, M Decker, E Rusak, DN Neshev, I Brener, YS Kivshar
Article, 0
6
Tailoring the enhancement of and interference among higher order multipole transitions in molecules with a plasmonic nanoantenna
E Rusak, J Straubel, P Gladysz, M Göddel, A Kedziorski, M Kühn, ...
arXiv preprint arXiv:1905.08482, 2019
42019
ImageNet-D: A new challenging robustness dataset inspired by domain adaptation
E Rusak, S Schneider, PV Gehler, O Bringmann, W Brendel, M Bethge
ICML 2022 Shift Happens Workshop, 2022
22022
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