431 citations · 2.5k across the 40 of their papers we have counts for
6 papers · 1 filter
Representation learning from videos in-the-wild: An object-centric approach
Rob Romijnders, Aravindh Mahendran, Michael Tschannen +4
We propose a method to learn image representations from uncurated videos. We combine a supervised loss from off-the-shelf object detectors and self-supervised losses which naturall…
On Robustness and Transferability of Convolutional Neural Networks
Josip Djolonga, Jessica Yung, Michael Tschannen +11
Modern deep convolutional networks (CNNs) are often criticized for not generalizing under distributional shifts. However, several recent breakthroughs in transfer learning suggest…
High-Fidelity Generative Image Compression
Fabian Mentzer, George Toderici, Michael Tschannen +1
We extensively study how to combine Generative Adversarial Networks and learned compression to obtain a state-of-the-art generative lossy compression system. In particular, we inve…
Learning Better Lossless Compression Using Lossy Compression
Fabian Mentzer, Luc Van Gool, Michael Tschannen
We leverage the powerful lossy image compression algorithm BPG to build a lossless image compression system. Specifically, the original image is first decomposed into the lossy rec…
Automatic Shortcut Removal for Self-Supervised Representation Learning
Matthias Minderer, Olivier Bachem, Neil Houlsby +1
In self-supervised visual representation learning, a feature extractor is trained on a "pretext task" for which labels can be generated cheaply, without human annotation. A central…
Weakly-Supervised Disentanglement Without Compromises
Francesco Locatello, Ben Poole, Gunnar Rätsch +3
Intelligent agents should be able to learn useful representations by observing changes in their environment. We model such observations as pairs of non-i.i.d. images sharing at lea…