| Image style transfer using convolutional neural networks LA Gatys, AS Ecker, M Bethge Proceedings of the IEEE conference on computer vision and pattern …, 2016 | 10183* | 2016 |
| Texture synthesis using convolutional neural networks L Gatys, AS Ecker, M Bethge Advances in neural information processing systems 28, 2015 | 1844 | 2015 |
| Controlling perceptual factors in neural style transfer LA Gatys, AS Ecker, M Bethge, A Hertzmann, E Shechtman Proceedings of the IEEE conference on computer vision and pattern …, 2017 | 725* | 2017 |
| Deep convolutional models improve predictions of macaque V1 responses to natural images SA Cadena, GH Denfield, EY Walker, LA Gatys, AS Tolias, M Bethge, ... PLoS computational biology 15 (4), e1006897, 2019 | 417 | 2019 |
| Understanding low-and high-level contributions to fixation prediction M Kummerer, TSA Wallis, LA Gatys, M Bethge Proceedings of the IEEE international conference on computer vision, 4789-4798, 2017 | 373 | 2017 |
| Texture and art with deep neural networks LA Gatys, AS Ecker, M Bethge Current opinion in neurobiology 46, 178-186, 2017 | 103 | 2017 |
| What does it take to generate natural textures? I Ustyuzhaninov, W Brendel, L Gatys, M Bethge International conference on learning representations, 2017 | 86* | 2017 |
| App usage predicts cognitive ability in older adults ML Gordon, L Gatys, C Guestrin, JP Bigham, A Trister, K Patel Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems …, 2019 | 68 | 2019 |
| A parametric texture model based on deep convolutional features closely matches texture appearance for humans TSA Wallis, CM Funke, AS Ecker, LA Gatys, FA Wichmann, M Bethge Journal of vision 17 (12), 5-5, 2017 | 64 | 2017 |
| Image content is more important than Bouma’s Law for scene metamers TSA Wallis, CM Funke, AS Ecker, LA Gatys, FA Wichmann, M Bethge ELife 8, e42512, 2019 | 51 | 2019 |
| Machine learning assisted image prediction CE Guestrin, LA Gatys, SV Joshi, GM Larsson, KR Watson, S Sridhar, ... US Patent 11,386,355, 2022 | 38 | 2022 |
| Diverse feature visualizations reveal invariances in early layers of deep neural networks SA Cadena, MA Weis, LA Gatys, M Bethge, AS Ecker Proceedings of the European Conference on Computer Vision (ECCV), 217-232, 2018 | 33 | 2018 |
| Synthesising dynamic textures using convolutional neural networks CM Funke, LA Gatys, AS Ecker, M Bethge arXiv preprint arXiv:1702.07006, 2017 | 25 | 2017 |
| Guiding human gaze with convolutional neural networks LA Gatys, M Kümmerer, TSA Wallis, M Bethge arXiv preprint arXiv:1712.06492, 2017 | 19 | 2017 |
| Model-based metrics: Sample-efficient estimates of predictive model subpopulation performance AC Miller, LA Gatys, J Futoma, E Fox Machine Learning for Healthcare Conference, 308-336, 2021 | 9 | 2021 |
| Synaptic unreliability facilitates information transmission in balanced cortical populations LA Gatys, AS Ecker, T Tchumatchenko, M Bethge Physical Review E 91 (6), 062707, 2015 | 8 | 2015 |
| Modeling patterns of smartphone usage and their relationship to cognitive health J Rauber, EB Fox, LA Gatys Machine Learning for Health Workshop, NeurIPS 2019, 2019 | 7 | 2019 |
| Towards matching peripheral appearance for arbitrary natural images using deep features T Wallis, C Funke, A Ecker, L Gatys, F Wichmann, M Bethge Journal of Vision 17 (10), 786-786, 2017 | 6 | 2017 |
| Texture synthesis and style transfer using perceptual image representations from convolutional neural networks LA Gatys Tübingen, 2017 | 5 | 2017 |
| Introduction to NIPS 2017 Competition Track S Escalera, M Weimer, M Burtsev, V Malykh, V Logacheva, R Lowe, ... The NIPS'17 Competition: Building Intelligent Systems, 1-23, 2018 | 2 | 2018 |
Matthias BethgeTübingen University & Maddox Co-Founderbethgelab.org의 이메일 확인됨
