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Michael Tschannen
Michael Tschannen
Google DeepMind
Email confirmado em google.com - Página inicial
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Born again neural networks
T Furlanello, ZC Lipton, M Tschannen, L Itti, A Anandkumar
International Conference on Machine Learning (ICML), 1602-1611, 2018
12632018
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
6972019
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
6322017
Recent advances in autoencoder-based representation learning
M Tschannen, O Bachem, M Lucic
Workshop on Bayesian Deep Learning (NeurIPS 2018), 2018
6222018
On mutual information maximization for representation learning
M Tschannen*, J Djolonga*, PK Rubenstein, S Gelly, M Lucic
International Conference on Learning Representations (ICLR), 2020
6102020
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
5922018
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
5532023
High-Fidelity Generative Image Compression
F Mentzer, G Toderici, M Tschannen, E Agustsson
Advances in Neural Information Processing Systems (NeurIPS), 2020
4952020
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
455*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
3612020
Convolutional recurrent neural networks for electrocardiogram classification
M Zihlmann, D Perekrestenko, M Tschannen
Computing in Cardiology Conference (CinC) 44, 2017
3122017
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
2442019
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
2082020
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
1972018
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
179*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
1692024
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
1592021
Deep generative models for distribution-preserving lossy compression
M Tschannen, E Agustsson, M Lucic
Advances in Neural Information Processing Systems (NeurIPS), 2018
1492018
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
1232016
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
1162024
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