| Born again neural networks T Furlanello, ZC Lipton, M Tschannen, L Itti, A Anandkumar International Conference on Machine Learning (ICML), 1602-1611, 2018 | 1254 | 2018 |
| Generative adversarial networks for extreme learned image compression E Agustsson*, M Tschannen*, F Mentzer*, R Timofte, L Van Gool International Conference on Computer Vision (ICCV), 2019 | 694 | 2019 |
| Soft-to-hard vector quantization for end-to-end learning compressible representations E Agustsson, F Mentzer, M Tschannen, L Cavigelli, R Timofte, L Benini, ... Advances in Neural Information Processing Systems (NIPS), 1141-1151, 2017 | 626 | 2017 |
| Recent advances in autoencoder-based representation learning M Tschannen, O Bachem, M Lucic Workshop on Bayesian Deep Learning (NeurIPS 2018), 2018 | 613 | 2018 |
| On mutual information maximization for representation learning M Tschannen*, J Djolonga*, PK Rubenstein, S Gelly, M Lucic International Conference on Learning Representations (ICLR), 2020 | 604 | 2020 |
| Conditional probability models for deep image compression F Mentzer, E Agustsson, M Tschannen, R Timofte, L Van Gool IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018 | 590 | 2018 |
| Scaling vision transformers to 22 billion parameters M Dehghani, J Djolonga, B Mustafa, P Padlewski, J Heek, J Gilmer, ... International Conference on Machine Learning (ICML), 7480-7512, 2023 | 527 | 2023 |
| High-Fidelity Generative Image Compression F Mentzer, G Toderici, M Tschannen, E Agustsson Advances in Neural Information Processing Systems (NeurIPS), 2020 | 490 | 2020 |
| A large-scale study of representation learning with the visual task adaptation benchmark X Zhai, J Puigcerver, A Kolesnikov, P Ruyssen, C Riquelme, M Lucic, ... arXiv preprint arXiv:1910.04867, 2019 | 446* | 2019 |
| Weakly-supervised disentanglement without compromises F Locatello, B Poole, G Rätsch, B Schölkopf, O Bachem, M Tschannen International Conference on Machine Learning (ICML), 2020 | 361 | 2020 |
| Convolutional recurrent neural networks for electrocardiogram classification M Zihlmann, D Perekrestenko, M Tschannen Computing in Cardiology Conference (CinC) 44, 2017 | 312 | 2017 |
| Practical full resolution learned lossless image compression F Mentzer, E Agustsson, M Tschannen, R Timofte, L Van Gool IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2019 | 242 | 2019 |
| Disentangling factors of variation using few labels F Locatello, M Tschannen, S Bauer, G Rätsch, B Schölkopf, O Bachem International Conference on Learning Representations (ICLR), 2020 | 207 | 2020 |
| Towards image understanding from deep compression without decoding R Torfason, F Mentzer, E Agustsson, M Tschannen, R Timofte, L Van Gool International Conference on Learning Representations (ICLR), 2018 | 197 | 2018 |
| High-fidelity image generation with fewer labels M Lucic*, M Tschannen*, M Ritter*, X Zhai, O Bachem, S Gelly International Conference on Machine Learning (ICML), 2019 | 180* | 2019 |
| PaLI-X: On scaling up a multilingual vision and language model X Chen, J Djolonga, P Padlewski, B Mustafa, S Changpinyo, J Wu, ... IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2024 | 167 | 2024 |
| On Robustness and Transferability of Convolutional Neural Networks J Djolonga*, J Yung*, M Tschannen*, R Romijnders, L Beyer, ... IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2021 | 157 | 2021 |
| Deep generative models for distribution-preserving lossy compression M Tschannen, E Agustsson, M Lucic Advances in Neural Information Processing Systems (NeurIPS), 2018 | 149 | 2018 |
| Heart sound classification using deep structured features M Tschannen, T Kramer, G Marti, M Heinzmann, T Wiatowski Computing in Cardiology Conference (CinC), 565-568, 2016 | 121 | 2016 |
| PaliGemma: A versatile 3B VLM for transfer L Beyer, A Steiner, AS Pinto, A Kolesnikov, X Wang, D Salz, M Neumann, ... arXiv preprint arXiv:2407.07726, 2024 | 107 | 2024 |
Fabian MentzerResearch Scientist at Google Deep MindVerified email at google.com