Tschannen, Michael

31 publications

TMLR 2025 Jet: A Modern Transformer-Based Normalizing Flow Alexander Kolesnikov, André Susano Pinto, Michael Tschannen

ICLR 2025 JetFormer: An Autoregressive Generative Model of Raw Images and Text Michael Tschannen, André Susano Pinto, Alexander Kolesnikov

NeurIPS 2025 Quantization-Free Autoregressive Action Transformer Ziyad Sheebaelhamd, Michael Tschannen, Michael Muehlebach, Claire Vernade

ICLR 2024 Finite Scalar Quantization: VQ-VAE Made Simple Fabian Mentzer, David Minnen, Eirikur Agustsson, Michael Tschannen

ECCV 2024 GIVT: Generative Infinite-Vocabulary Transformers Michael Tschannen, Cian Eastwood, Fabian Mentzer

NeurIPS 2024 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, Xiaohua Zhai

CVPR 2024 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, Aj 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

TMLR 2024 Towards Truly Zero-Shot Compositional Visual Reasoning with LLMs as Programmers Aleksandar Stanić, Sergi Caelles, Michael Tschannen

CVPR 2023 CLIPPO: Image-and-Language Understanding from Pixels Only Michael Tschannen, Basil Mustafa, Neil Houlsby

CVPR 2023 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, Filip Pavetic

NeurIPS 2023 Image Captioners Are Scalable Vision Learners Too Michael Tschannen, Manoj Kumar, Andreas Steiner, Xiaohua Zhai, Neil Houlsby, Lucas Beyer

ICCV 2023 M2T: Masking Transformers Twice for Faster Decoding Fabian Mentzer, Eirikur Agustson, Michael Tschannen

ICML 2023 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 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

CVPRW 2022 Neural Face Video Compression Using Multiple Views Anna Volokitin, Stefan Brugger, Ali Benlalah, Sebastian Martin, Brian Amberg, Michael Tschannen

CVPR 2021 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, Sylvain Gelly, Neil Houlsby, Xiaohua Zhai, Mario Lucic

WACV 2021 Representation Learning from Videos In-the-Wild: An Object-Centric Approach Rob Romijnders, Aravindh Mahendran, Michael Tschannen, Josip Djolonga, Marvin Ritter, Neil Houlsby, Mario Lucic

ICML 2020 Automatic Shortcut Removal for Self-Supervised Representation Learning Matthias Minderer, Olivier Bachem, Neil Houlsby, Michael Tschannen

ICLR 2020 Disentangling Factors of Variations Using Few Labels Francesco Locatello, Michael Tschannen, Stefan Bauer, Gunnar Rätsch, Bernhard Schölkopf, Olivier Bachem

NeurIPS 2020 High-Fidelity Generative Image Compression Fabian Mentzer, George D Toderici, Michael Tschannen, Eirikur Agustsson

ICLR 2020 On Mutual Information Maximization for Representation Learning Michael Tschannen, Josip Djolonga, Paul K. Rubenstein, Sylvain Gelly, Mario Lucic

ICML 2020 Weakly-Supervised Disentanglement Without Compromises Francesco Locatello, Ben Poole, Gunnar Raetsch, Bernhard Schölkopf, Olivier Bachem, Michael Tschannen

ICLRW 2019 Disentangling Factors of Variations Using Few Labels Francesco Locatello, Michael Tschannen, Stefan Bauer, Gunnar R¨¨ätsch, Bernhard Schölkopf, Olivier Bachem

ICML 2018 Born Again Neural Networks Tommaso Furlanello, Zachary Lipton, Michael Tschannen, Laurent Itti, Anima Anandkumar

NeurIPS 2018 Deep Generative Models for Distribution-Preserving Lossy Compression Michael Tschannen, Eirikur Agustsson, Mario Lucic

CVPRW 2018 Extreme Learned Image Compression with GANs Eirikur Agustsson, Michael Tschannen, Fabian Mentzer, Radu Timofte, Luc Van Gool

ICML 2018 StrassenNets: Deep Learning with a Multiplication Budget Michael Tschannen, Aran Khanna, Animashree Anandkumar

ICLR 2018 Towards Image Understanding from Deep Compression Without Decoding Robert Torfason, Fabian Mentzer, Eirikur Agustsson, Michael Tschannen, Radu Timofte, Luc Van Gool

AISTATS 2017 A Unified Optimization View on Generalized Matching Pursuit and Frank-Wolfe Francesco Locatello, Rajiv Khanna, Michael Tschannen, Martin Jaggi

NeurIPS 2017 Greedy Algorithms for Cone Constrained Optimization with Convergence Guarantees Francesco Locatello, Michael Tschannen, Gunnar Raetsch, Martin Jaggi

NeurIPS 2017 Soft-to-Hard Vector Quantization for End-to-End Learning Compressible Representations Eirikur Agustsson, Fabian Mentzer, Michael Tschannen, Lukas Cavigelli, Radu Timofte, Luca Benini, Luc V. Gool

ICML 2016 Discrete Deep Feature Extraction: A Theory and New Architectures Thomas Wiatowski, Michael Tschannen, Aleksandar Stanic, Philipp Grohs, Helmut Boelcskei