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Representation learning from videos in-the-wild: An object-centric approach


Oct 06, 2020
Rob Romijnders, Aravindh Mahendran, Michael Tschannen, Josip Djolonga, Marvin Ritter, Neil Houlsby, Mario Lucic


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On Robustness and Transferability of Convolutional Neural Networks


Jul 16, 2020
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, Mario Lucic


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High-Fidelity Generative Image Compression


Jul 10, 2020
Fabian Mentzer, George Toderici, Michael Tschannen, Eirikur Agustsson

* Project page: https://hific.github.io 

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Learning Better Lossless Compression Using Lossy Compression


Mar 23, 2020
Fabian Mentzer, Luc Van Gool, Michael Tschannen

* CVPR'20 camera-ready version 

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Automatic Shortcut Removal for Self-Supervised Representation Learning


Feb 21, 2020
Matthias Minderer, Olivier Bachem, Neil Houlsby, Michael Tschannen


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Weakly-Supervised Disentanglement Without Compromises


Feb 07, 2020
Francesco Locatello, Ben Poole, Gunnar Rätsch, Bernhard Schölkopf, Olivier Bachem, Michael Tschannen


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Self-Supervised Learning of Video-Induced Visual Invariances


Dec 05, 2019
Michael Tschannen, Josip Djolonga, Marvin Ritter, Aravindh Mahendran, Neil Houlsby, Sylvain Gelly, Mario Lucic


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Semantic Bottleneck Scene Generation


Nov 26, 2019
Samaneh Azadi, Michael Tschannen, Eric Tzeng, Sylvain Gelly, Trevor Darrell, Mario Lucic


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The Visual Task Adaptation Benchmark


Oct 01, 2019
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, Neil Houlsby


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On Mutual Information Maximization for Representation Learning


Jul 31, 2019
Michael Tschannen, Josip Djolonga, Paul K. Rubenstein, Sylvain Gelly, Mario Lucic

* Michael Tschannen and Josip Djolonga contributed equally 

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Disentangling Factors of Variation Using Few Labels


May 03, 2019
Francesco Locatello, Michael Tschannen, Stefan Bauer, Gunnar Rätsch, Bernhard Schölkopf, Olivier Bachem


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High-Fidelity Image Generation With Fewer Labels


Mar 06, 2019
Mario Lucic, Michael Tschannen, Marvin Ritter, Xiaohua Zhai, Olivier Bachem, Sylvain Gelly

* Mario Lucic, Michael Tschannen, and Marvin Ritter contributed equally to this work 

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Recent Advances in Autoencoder-Based Representation Learning


Dec 12, 2018
Michael Tschannen, Olivier Bachem, Mario Lucic

* Presented at the third workshop on Bayesian Deep Learning (NeurIPS 2018) 

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Practical Full Resolution Learned Lossless Image Compression


Nov 30, 2018
Fabian Mentzer, Eirikur Agustsson, Michael Tschannen, Radu Timofte, Luc Van Gool


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Deep Generative Models for Distribution-Preserving Lossy Compression


Oct 28, 2018
Michael Tschannen, Eirikur Agustsson, Mario Lucic

* NIPS 2018. Code: https://github.com/mitscha/dplc . Changes w.r.t. v1: Some clarifications in the text and additional numerical results 

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Generative Adversarial Networks for Extreme Learned Image Compression


Oct 23, 2018
Eirikur Agustsson, Michael Tschannen, Fabian Mentzer, Radu Timofte, Luc Van Gool

* EA, MT, and FM contributed equally. Project website: https://data.vision.ee.ethz.ch/aeirikur/extremecompression 

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Born Again Neural Networks


Jun 29, 2018
Tommaso Furlanello, Zachary C. Lipton, Michael Tschannen, Laurent Itti, Anima Anandkumar

* Published @ICML 2018 

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Noisy subspace clustering via matching pursuits


Jun 08, 2018
Michael Tschannen, Helmut Bölcskei

* IEEE Transactions on Information Theory, Vol. 64, No. 6, pp. 4081-4104, June 2018 
* 24 pages, 5 figures 

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StrassenNets: Deep Learning with a Multiplication Budget


Jun 08, 2018
Michael Tschannen, Aran Khanna, Anima Anandkumar

* ICML 2018. Code available at https://github.com/mitscha/strassennets 

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Conditional Probability Models for Deep Image Compression


Jun 04, 2018
Fabian Mentzer, Eirikur Agustsson, Michael Tschannen, Radu Timofte, Luc Van Gool

* CVPR 2018. Code available at https://github.com/fab-jul/imgcomp-cvpr . The first two authors contributed equally 

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Convolutional Recurrent Neural Networks for Electrocardiogram Classification


Apr 09, 2018
Martin Zihlmann, Dmytro Perekrestenko, Michael Tschannen

* 4 pages, in Computing in Cardiology (CinC) 2017, PhysioNet/CinC Challenge 2017 submission. Code available at https://github.com/yruffiner/ecg-classification 

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Towards Image Understanding from Deep Compression without Decoding


Mar 16, 2018
Robert Torfason, Fabian Mentzer, Eirikur Agustsson, Michael Tschannen, Radu Timofte, Luc Van Gool

* ICLR 2018 conference paper 

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Greedy Algorithms for Cone Constrained Optimization with Convergence Guarantees


Nov 19, 2017
Francesco Locatello, Michael Tschannen, Gunnar Rätsch, Martin Jaggi

* NIPS 2017 

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Robust nonparametric nearest neighbor random process clustering


Sep 28, 2017
Michael Tschannen, Helmut Bölcskei

* IEEE Transactions on Signal Processing, Vol. 65, No. 22, pp. 6009-6023, Nov. 2017 
* 15 pages, 7 figures 

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Deep Structured Features for Semantic Segmentation


Jun 16, 2017
Michael Tschannen, Lukas Cavigelli, Fabian Mentzer, Thomas Wiatowski, Luca Benini

* EUSIPCO 2017, 5 pages, 2 figures 

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Soft-to-Hard Vector Quantization for End-to-End Learning Compressible Representations


Jun 08, 2017
Eirikur Agustsson, Fabian Mentzer, Michael Tschannen, Lukas Cavigelli, Radu Timofte, Luca Benini, Luc Van Gool


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A Unified Optimization View on Generalized Matching Pursuit and Frank-Wolfe


Mar 07, 2017
Francesco Locatello, Rajiv Khanna, Michael Tschannen, Martin Jaggi

* appearing at AISTATS 2017 

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Discrete Deep Feature Extraction: A Theory and New Architectures


May 26, 2016
Thomas Wiatowski, Michael Tschannen, Aleksandar Stanić, Philipp Grohs, Helmut Bölcskei

* Proc. of International Conference on Machine Learning (ICML), New York, USA, pp. 2149-2158, June 2016 
* Proc. of International Conference on Machine Learning (ICML), New York, USA, June 2016, to appear 

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