Michael Tschannen

ORCID

Publication Activity (10 Years)

Years Active: 2012-2024
Publications (10 Years): 76

Top Venues

ETH Zurich, Zürich, Switzerland (PHD)

Education History

ETH Zurich, Zürich, Switzerland

PHD

Publications

Bo Wan, Michael Tschannen, Yongqin Xian, Filip Pavetic, Ibrahim Alabdulmohsin, Xiao Wang, André Susano Pinto, Andreas Steiner, Lucas Beyer, Xiaohua Zhai
LocCa: Visual Pretraining with Location-aware Captioners. CoRR (2024)
Aleksandar Stanic, Sergi Caelles, Michael Tschannen
Towards Truly Zero-shot Compositional Visual Reasoning with LLMs as Programmers. CoRR (2024)
Aleksandar Stanic, Sergi Caelles, Michael Tschannen
Towards Truly Zero-shot Compositional Visual Reasoning with LLMs as Programmers. Trans. Mach. Learn. Res. 2024 (2024)
Fabian Mentzer, David Minnen, Eirikur Agustsson, Michael Tschannen
Finite Scalar Quantization: VQ-VAE Made Simple. ICLR (2024)
Lucas Beyer, Andreas Steiner, André Susano Pinto, Alexander Kolesnikov, Xiao Wang, Daniel Salz, Maxim Neumann, Ibrahim Alabdulmohsin, Michael Tschannen, Emanuele Bugliarello, Thomas Unterthiner, Daniel Keysers, Skanda Koppula, Fangyu Liu, Adam Grycner, Alexey A. Gritsenko, Neil Houlsby, Manoj Kumar, Keran Rong, Julian Eisenschlos, Rishabh Kabra, Matthias Bauer, Matko Bosnjak, Xi Chen, Matthias Minderer, Paul Voigtlaender, Ioana Bica, Ivana Balazevic, Joan Puigcerver, Pinelopi Papalampidi, Olivier J. Hénaff, Xi Xiong, Radu Soricut, Jeremiah Harmsen, Xiaohua Zhai
PaliGemma: A versatile 3B VLM for transfer. CoRR (2024)
Lucas Beyer, Pavel Izmailov, Alexander Kolesnikov, Mathilde Caron, Simon Kornblith, Xiaohua Zhai, Matthias Minderer, Michael Tschannen, Ibrahim Alabdulmohsin, Filip Pavetic
FlexiViT: One Model for All Patch Sizes. CVPR (2023)
Mostafa Dehghani, Josip Djolonga, Basil Mustafa, Piotr Padlewski, Jonathan Heek, Justin Gilmer, Andreas Steiner, Mathilde Caron, Robert Geirhos, Ibrahim Alabdulmohsin, Rodolphe Jenatton, Lucas Beyer, Michael Tschannen, Anurag Arnab, Xiao Wang, Carlos Riquelme, Matthias Minderer, Joan Puigcerver, Utku Evci, Manoj Kumar, Sjoerd van Steenkiste, Gamaleldin F. Elsayed, Aravindh Mahendran, Fisher Yu, Avital Oliver, Fantine Huot, Jasmijn Bastings, Mark Patrick Collier, Alexey A. Gritsenko, Vighnesh Birodkar, Cristina Vasconcelos, Yi Tay, Thomas Mensink, Alexander Kolesnikov, Filip Pavetic, Dustin Tran, Thomas Kipf, Mario Lucic, Xiaohua Zhai, Daniel Keysers, Jeremiah Harmsen, Neil Houlsby
Scaling Vision Transformers to 22 Billion Parameters. CoRR (2023)
Fabian Mentzer, David Minnen, Eirikur Agustsson, Michael Tschannen
Finite Scalar Quantization: VQ-VAE Made Simple. CoRR (2023)
Fabian Mentzer, Eirikur Agustsson, Michael Tschannen
M2T: Masking Transformers Twice for Faster Decoding. ICCV (2023)
Michael Tschannen, Manoj Kumar, Andreas Steiner, Xiaohua Zhai, Neil Houlsby, Lucas Beyer
Image Captioners Are Scalable Vision Learners Too. NeurIPS (2023)
Fabian Mentzer, Eirikur Agustsson, Michael Tschannen
M2T: Masking Transformers Twice for Faster Decoding. CoRR (2023)
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 van 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, Neil Houlsby
Scaling Vision Transformers to 22 Billion Parameters. ICML (2023)
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, Radu Soricut
PaLI-X: On Scaling up a Multilingual Vision and Language Model. CoRR (2023)
Michael Tschannen, Manoj Kumar, Andreas Steiner, Xiaohua Zhai, Neil Houlsby, Lucas Beyer
Image Captioners Are Scalable Vision Learners Too. CoRR (2023)
Michael Tschannen, Basil Mustafa, Neil Houlsby
CLIPPO: Image-and-Language Understanding from Pixels Only. CVPR (2023)
Michael Tschannen, Cian Eastwood, Fabian Mentzer
GIVT: Generative Infinite-Vocabulary Transformers. CoRR (2023)
Anna Volokitin, Stefan Brugger, Ali Benlalah, Sebastian Martin, Brian Amberg, Michael Tschannen
Neural Face Video Compression using Multiple Views. CVPR Workshops (2022)
Anna Volokitin, Stefan Brugger, Ali Benlalah, Sebastian Martin, Brian Amberg, Michael Tschannen
Neural Face Video Compression using Multiple Views. CoRR (2022)
Michael Tschannen, Basil Mustafa, Neil Houlsby
Image-and-Language Understanding from Pixels Only. CoRR (2022)
Lucas Beyer, Pavel Izmailov, Alexander Kolesnikov, Mathilde Caron, Simon Kornblith, Xiaohua Zhai, Matthias Minderer, Michael Tschannen, Ibrahim Alabdulmohsin, Filip Pavetic
FlexiViT: One Model for All Patch Sizes. CoRR (2022)
Rob Romijnders, Aravindh Mahendran, Michael Tschannen, Josip Djolonga, Marvin Ritter, Neil Houlsby, Mario Lucic
Representation learning from videos in-the-wild: An object-centric approach. WACV (2021)
Josip Djolonga, Jessica Yung, Michael Tschannen, Rob Romijnders, Lucas Beyer, Alexander Kolesnikov, Joan Puigcerver, Matthias Minderer, Alexander D'Amour, Dan Moldovan, Sylvain Gelly, Neil Houlsby, Xiaohua Zhai, Mario Lucic
On Robustness and Transferability of Convolutional Neural Networks. CVPR (2021)
Fabian Mentzer, George Toderici, Michael Tschannen, Eirikur Agustsson
High-Fidelity Generative Image Compression. CoRR (2020)
Francesco Locatello, Ben Poole, Gunnar Rätsch, Bernhard Schölkopf, Olivier Bachem, Michael Tschannen
Weakly-Supervised Disentanglement Without Compromises. ICML (2020)
Michael Tschannen, Josip Djolonga, Paul K. Rubenstein, Sylvain Gelly, Mario Lucic
On Mutual Information Maximization for Representation Learning. ICLR (2020)
Francesco Locatello, Michael Tschannen, Stefan Bauer, Gunnar Rätsch, Bernhard Schölkopf, Olivier Bachem
Disentangling Factors of Variations Using Few Labels. ICLR (2020)
Fabian Mentzer, George Toderici, Michael Tschannen, Eirikur Agustsson
High-Fidelity Generative Image Compression. NeurIPS (2020)
Josip Djolonga, Jessica Yung, Michael Tschannen, Rob Romijnders, Lucas Beyer, Alexander Kolesnikov, Joan Puigcerver, Matthias Minderer, Alexander D'Amour, Dan Moldovan, Sylvain Gelly, Neil Houlsby, Xiaohua Zhai, Mario Lucic
On Robustness and Transferability of Convolutional Neural Networks. CoRR (2020)
Francesco Locatello, Ben Poole, Gunnar Rätsch, Bernhard Schölkopf, Olivier Bachem, Michael Tschannen
Weakly-Supervised Disentanglement Without Compromises. CoRR (2020)
Matthias Minderer, Olivier Bachem, Neil Houlsby, Michael Tschannen
Automatic Shortcut Removal for Self-Supervised Representation Learning. CoRR (2020)
Rob Romijnders, Aravindh Mahendran, Michael Tschannen, Josip Djolonga, Marvin Ritter, Neil Houlsby, Mario Lucic
Representation learning from videos in-the-wild: An object-centric approach. CoRR (2020)
Matthias Minderer, Olivier Bachem, Neil Houlsby, Michael Tschannen
Automatic Shortcut Removal for Self-Supervised Representation Learning. ICML (2020)
Fabian Mentzer, Luc Van Gool, Michael Tschannen
Learning Better Lossless Compression Using Lossy Compression. CoRR (2020)
Fabian Mentzer, Luc Van Gool, Michael Tschannen
Learning Better Lossless Compression Using Lossy Compression. CVPR (2020)
Michael Tschannen, Josip Djolonga, Marvin Ritter, Aravindh Mahendran, Neil Houlsby, Sylvain Gelly, Mario Lucic
Self-Supervised Learning of Video-Induced Visual Invariances. CVPR (2020)
Mario Lucic, Michael Tschannen, Marvin Ritter, Xiaohua Zhai, Olivier Bachem, Sylvain Gelly
High-Fidelity Image Generation With Fewer Labels. ICML (2019)
Mario Lucic, Michael Tschannen, Marvin Ritter, Xiaohua Zhai, Olivier Bachem, Sylvain Gelly
High-Fidelity Image Generation With Fewer Labels. CoRR (2019)
Fabian Mentzer, Eirikur Agustsson, Michael Tschannen, Radu Timofte, Luc Van Gool
Practical Full Resolution Learned Lossless Image Compression. CVPR (2019)
Xiaohua Zhai, Joan Puigcerver, Alexander Kolesnikov, Pierre Ruyssen, Carlos Riquelme, Mario Lucic, Josip Djolonga, André Susano Pinto, Maxim Neumann, Alexey Dosovitskiy, Lucas Beyer, Olivier Bachem, Michael Tschannen, Marcin Michalski, Olivier Bousquet, Sylvain Gelly, Neil Houlsby
The Visual Task Adaptation Benchmark. CoRR (2019)
Francesco Locatello, Michael Tschannen, Stefan Bauer, Gunnar Rätsch, Bernhard Schölkopf, Olivier Bachem
Disentangling Factors of Variation Using Few Labels. CoRR (2019)
Michael Tschannen, Josip Djolonga, Paul K. Rubenstein, Sylvain Gelly, Mario Lucic
On Mutual Information Maximization for Representation Learning. CoRR (2019)
Eirikur Agustsson, Michael Tschannen, Fabian Mentzer, Radu Timofte, Luc Van Gool
Generative Adversarial Networks for Extreme Learned Image Compression. ICCV (2019)
Michael Tschannen, Josip Djolonga, Marvin Ritter, Aravindh Mahendran, Neil Houlsby, Sylvain Gelly, Mario Lucic
Self-Supervised Learning of Video-Induced Visual Invariances. CoRR (2019)
Samaneh Azadi, Michael Tschannen, Eric Tzeng, Sylvain Gelly, Trevor Darrell, Mario Lucic
Semantic Bottleneck Scene Generation. CoRR (2019)
Eirikur Agustsson, Michael Tschannen, Fabian Mentzer, Radu Timofte, Luc Van Gool
Generative Adversarial Networks for Extreme Learned Image Compression. CoRR (2018)
Michael Tschannen, Eirikur Agustsson, Mario Lucic
Deep Generative Models for Distribution-Preserving Lossy Compression. CoRR (2018)
Tommaso Furlanello, Zachary Chase Lipton, Michael Tschannen, Laurent Itti, Anima Anandkumar
Born-Again Neural Networks. ICML (2018)
Michael Tschannen, Olivier Bachem, Mario Lucic
Recent Advances in Autoencoder-Based Representation Learning. CoRR (2018)
Michael Tschannen, Aran Khanna, Animashree Anandkumar
StrassenNets: Deep Learning with a Multiplication Budget. ICML (2018)
Fabian Mentzer, Eirikur Agustsson, Michael Tschannen, Radu Timofte, Luc Van Gool
Conditional Probability Models for Deep Image Compression. CoRR (2018)
Tommaso Furlanello, Zachary C. Lipton, Michael Tschannen, Laurent Itti, Anima Anandkumar
Born Again Neural Networks. CoRR (2018)
Robert Torfason, Fabian Mentzer, Eirikur Agustsson, Michael Tschannen, Radu Timofte, Luc Van Gool
Towards Image Understanding from Deep Compression Without Decoding. ICLR (Poster) (2018)
Michael Tschannen, Helmut Bölcskei
Noisy Subspace Clustering via Matching Pursuits. IEEE Trans. Inf. Theory 64 (6) (2018)
Fabian Mentzer, Eirikur Agustsson, Michael Tschannen, Radu Timofte, Luc Van Gool
Practical Full Resolution Learned Lossless Image Compression. CoRR (2018)
Eirikur Agustsson, Michael Tschannen, Fabian Mentzer, Radu Timofte, Luc Van Gool
Extreme Learned Image Compression with GANs. CVPR Workshops (2018)
Michael Tschannen, Eirikur Agustsson, Mario Lucic
Deep Generative Models for Distribution-Preserving Lossy Compression. NeurIPS (2018)
Robert Torfason, Fabian Mentzer, Eirikur Agustsson, Michael Tschannen, Radu Timofte, Luc Van Gool
Towards Image Understanding from Deep Compression without Decoding. CoRR (2018)
Fabian Mentzer, Eirikur Agustsson, Michael Tschannen, Radu Timofte, Luc Van Gool
Conditional Probability Models for Deep Image Compression. CVPR (2018)
Francesco Locatello, Michael Tschannen, Gunnar Rätsch, Martin Jaggi
Greedy Algorithms for Cone Constrained Optimization with Convergence Guarantees. NIPS (2017)
Francesco Locatello, Rajiv Khanna, Michael Tschannen, Martin Jaggi
A Unified Optimization View on Generalized Matching Pursuit and Frank-Wolfe. CoRR (2017)
Francesco Locatello, Rajiv Khanna, Michael Tschannen, Martin Jaggi
A Unified Optimization View on Generalized Matching Pursuit and Frank-Wolfe. AISTATS (2017)
Eirikur Agustsson, Fabian Mentzer, Michael Tschannen, Lukas Cavigelli, Radu Timofte, Luca Benini, Luc Van Gool
Soft-to-Hard Vector Quantization for End-to-End Learning Compressible Representations. NIPS (2017)
Michael Tschannen, Lukas Cavigelli, Fabian Mentzer, Thomas Wiatowski, Luca Benini
Deep structured features for semantic segmentation. EUSIPCO (2017)
Martin Zihlmann, Dmytro Perekrestenko, Michael Tschannen
Convolutional Recurrent Neural Networks for Electrocardiogram Classification. CoRR (2017)
Eirikur Agustsson, Fabian Mentzer, Michael Tschannen, Lukas Cavigelli, Radu Timofte, Luca Benini, Luc Van Gool
Soft-to-Hard Vector Quantization for End-to-End Learned Compression of Images and Neural Networks. CoRR (2017)
Michael Tschannen, Aran Khanna, Anima Anandkumar
StrassenNets: Deep learning with a multiplication budget. CoRR (2017)
Martin Zihlmann, Dmytro Perekrestenko, Michael Tschannen
Convolutional Recurrent Neural Networks for Electrocardiogram Classification. CinC (2017)
Francesco Locatello, Michael Tschannen, Gunnar Rätsch, Martin Jaggi
Greedy Algorithms for Cone Constrained Optimization with Convergence Guarantees. CoRR (2017)
Rajiv Khanna, Michael Tschannen, Martin Jaggi
Pursuits in Structured Non-Convex Matrix Factorizations. CoRR (2016)
Thomas Wiatowski, Michael Tschannen, Aleksandar Stanic, Philipp Grohs, Helmut Bölcskei
Discrete Deep Feature Extraction: A Theory and New Architectures. ICML (2016)
Michael Tschannen, Helmut Bölcskei
Noisy subspace clustering via matching pursuits. CoRR (2016)
Michael Tschannen, Lukas Cavigelli, Fabian Mentzer, Thomas Wiatowski, Luca Benini
Deep Structured Features for Semantic Segmentation. CoRR (2016)
Michael Tschannen, Thomas Kramer, Gian Marti, Matthias Heinzmann, Thomas Wiatowski
Heart Sound Classification Using Deep Structured Features. CinC (2016)
Thomas Wiatowski, Michael Tschannen, Aleksandar Stanic, Philipp Grohs, Helmut Bölcskei
Discrete Deep Feature Extraction: A Theory and New Architectures. CoRR (2016)
Michael Tschannen, Helmut Bölcskei
Robust nonparametric nearest neighbor random process clustering. CoRR (2016)
Michael Tschannen, Lazaros Vlachopoulos, Christian Gerber, Gábor Székely, Philipp Fürnstahl
Regression forest-based automatic estimation of the articular margin plane for shoulder prosthesis planning. Medical Image Anal. 31 (2016)
Michael Tschannen, Helmut Bölcskei
Nonparametric Nearest Neighbor Random Process Clustering. CoRR (2015)
Reinhard Heckel, Michael Tschannen, Helmut Bölcskei
Dimensionality-reduced subspace clustering. CoRR (2015)
Michael Tschannen, Helmut Bölcskei
Nonparametric nearest neighbor random process clustering. ISIT (2015)
Reinhard Heckel, Michael Tschannen, Helmut Bölcskei
Subspace clustering of dimensionality-reduced data. CoRR (2014)
Reinhard Heckel, Michael Tschannen, Helmut Bölcskei
Subspace clustering of dimensionality-reduced data. ISIT (2014)
Valeria De Luca, Michael Tschannen, Gábor Székely, Christine Tanner
A Learning-Based Approach for Fast and Robust Vessel Tracking in Long Ultrasound Sequences. MICCAI (1) (2013)
Michael Tschannen, Grzegorz Toporek, Daphné Wallach, Matthias Peterhans, Stefan Weber
Single Marker Localization for Automatic Patient Registration in Interventional Radiology. CURAC (2012)
Marc Rennhard, Michael Tschannen, Tobias Christen
SecureSafe: a highly secure online data safe industrial use case. MPM@EuroSys (2012)

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Publication Activity (10 Years)

Top Topics

Top Venues

Michael Tschannen