Michael Tschannen (original) (raw)

I’m a Research Scientist at Google DeepMind Zurich (formerly Google Brain) broadly interested in multimodal learning for understanding and generation tasks.

Before that I was working on computer vision R&D at Apple Zurich for two years, and spent a year as a postdoc at Google Research Zurich (Brain Team) exploring topics in unsupervised representation learning, generative models, and neural compression. I completed my PhD at ETH Zurich under the supervision ofHelmut Bölcskei in late 2018. Prior to that I obtained a MSc (with distinction) from ETH Zurichand a BSc from EPFL, both in Electrical Engineering and Information Technology. In fall 2017, I interned atAmazon AI in Palo Alto, CA, and in fall 2018 I was a part-time research consultant working with Google Research Zurich (Brain Team).

Contact: mi.gmail.com

News

Publications

*denotes equal contribution. See Google Scholar for a potentially more up-to-date list.

2024

1. LocCa: Visual Pretraining with Location-aware Captioners Bo Wan,Michael Tschannen, Yongqin Xian, Filip Pavetic, Ibrahim Alabdulmohsin, Xiao Wang, André Susano Pinto, Andreas Steiner, Lucas Beyer, and Xiaohua Zhai arXiv:2403.19596, 2024
2. PaLI-X: On scaling up a multilingual vision and language model Xi Chen, Josip Djolonga, Piotr Padlewski, Basil Mustafa, Soravit Changpinyo, Jialin Wu, Carlos Riquelme Ruiz, Sebastian Goodman, Xiao Wang, Yi Tay, Siamak Shakeri, Mostafa Dehghani, Daniel Salz, Mario Lucic,Michael Tschannen, Arsha Nagrani, Hexiang Hu, Mandar Joshi, Bo Pang, Ceslee Montgomery, Paulina Pietrzyk, Marvin Ritter, A. J. Piergiovanni, Matthias Minderer, Filip Pavetic, Austin Waters, Gang Li, Ibrahim Alabdulmohsin, Lucas Beyer, Julien Amelot, Kenton Lee, Andreas Peter Steiner, Yang Li, Daniel Keysers, Anurag Arnab, Yuanzhong Xu, Keran Rong, Alexander Kolesnikov, Mojtaba Seyedhosseini, Anelia Angelova, Xiaohua Zhai, Neil Houlsby, and Radu Soricut In Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2024

2023

1. Scaling vision transformers to 22 billion parameters Mostafa Dehghani, Josip Djolonga, Basil Mustafa, Piotr Padlewski, Jonathan Heek, Justin Gilmer, Andreas Peter Steiner, Mathilde Caron, Robert Geirhos, Ibrahim Alabdulmohsin, Rodolphe Jenatton, Lucas Beyer,Michael Tschannen, Anurag Arnab, Xiao Wang, Carlos Riquelme Ruiz, Matthias Minderer, Joan Puigcerver, Utku Evci, Manoj Kumar, Sjoerd Steenkiste, Gamaleldin Fathy Elsayed, Aravindh Mahendran, Fisher Yu, Avital Oliver, Fantine Huot, Jasmijn Bastings, Mark Collier, Alexey A. Gritsenko, Vighnesh Birodkar, Cristina Nader Vasconcelos, Yi Tay, Thomas Mensink, Alexander Kolesnikov, Filip Pavetic, Dustin Tran, Thomas Kipf, Mario Lucic, Xiaohua Zhai, Daniel Keysers, Jeremiah J. Harmsen, and Neil Houlsby In Proc. International Conference on Machine Learning (ICML), 2023
2. FlexiViT: One Model for All Patch Sizes Lucas Beyer, Pavel Izmailov, Alexander Kolesnikov, Mathilde Caron, Simon Kornblith, Xiaohua Zhai, Matthias Minderer,Michael Tschannen, Ibrahim Alabdulmohsin, and Filip Pavetic In Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2023 [code]

2022

1. Neural Face Video Compression using Multiple Views Anna Volokitin, Stefan Brugger, Ali Benlalah, Sebastian Martin, Brian Amberg, and Michael Tschannen In Proc. IEEE Conf. on Computer Vision and Pattern Recognition Workshops (CVPRW), 2022 Workshop and Challenge on Learned Image Compression (CLIC) Best Student Paper Award

2021

1. On Robustness and Transferability of Convolutional Neural Networks Josip Djolonga*, Jessica Yung*,Michael Tschannen*, Rob Romijnders, Lucas Beyer, Alexander Kolesnikov, Joan Puigcerver, Matthias Minderer, Alexander D’Amour, Dan Moldovan, Sylvan Gelly, Neil Houlsby, Xiaohua Zhai, and Mario Lucic In Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2021

2020

1. High-Fidelity Generative Image Compression Fabian Mentzer, George Toderici,Michael Tschannen, and Eirikur Agustsson In Advances in Neural Information Processing Systems (NeurIPS), 2020 oral presentation
2. Weakly-supervised disentanglement without compromises Francesco Locatello, Ben Poole, Gunnar Rätsch, Bernhard Schölkopf, Olivier Bachem, and Michael Tschannen In Proc. International Conference on Machine Learning (ICML), 2020
3. Self-supervised learning of video-induced visual invariances Michael Tschannen, Josip Djolonga, Marvin Ritter, Aravindh Mahendran, Xiaohua Zhai, Neil Houlsby, Sylvain Gelly, and Mario Lucic In Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2020
4. Disentangling factors of variation using few labels Francesco Locatello,Michael Tschannen, Stefan Bauer, Gunnar Rätsch, Bernhard Schölkopf, and Olivier Bachem In Proc. International Conference on Learning Representations (ICLR), 2020

2019

1. Semantic bottleneck scene generation Samaneh Azadi,Michael Tschannen, Eric Tzeng, Sylvain Gelly, Trevor Darrell, and Mario Lucic arXiv:1911.11357, 2019
2. The visual task adaptation benchmark Xiaohua Zhai, Joan Puigcerver, Alexander Kolesnikov, Pierre Ruyssen, Carlos Riquelme, Mario Lucic, Josip Djolonga, Andre Susano Pinto, Maxim Neumann, Alexey Dosovitskiy, Lucas Beyer, Olivier Bachem,Michael Tschannen, Marcin Michalski, Olivier Bousquet, Sylvain Gelly, and Neil Houlsby arXiv:1910.04867, 2019
3. Practical full resolution learned lossless image compression Fabian Mentzer, Eirikur Agustsson,Michael Tschannen, Radu Timofte, and Luc Van Gool In Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2019 oral presentation
4. High-fidelity image generation with fewer labels Mario Lucic*,Michael Tschannen*, Marvin Ritter*, Xiaohua Zhai, Olivier Bachem, and Sylvain Gelly In Proc. International Conference on Machine Learning (ICML), 2019

2018

1. Born-again neural networks Tommaso Furlanello, Zachary C. Lipton,Michael Tschannen, Laurent Itti, and Anima Anandkumar In Proc. International Conference on Machine Learning (ICML), 2018

2017

2016

2015

2014

2013