29 citations · 63 across the 4 of their papers we have counts for
5 papers
Projected GANs Converge Faster
Axel Sauer, Kashyap Chitta, Jens Müller +1
Generative Adversarial Networks (GANs) produce high-quality images but are challenging to train. They need careful regularization, vast amounts of compute, and expensive hyper-para…
Zoomorphic Gestures for Communicating Cobot States
Vanessa Sauer, Axel Sauer, Alexander Mertens
Communicating the robot state is vital to creating an efficient and trustworthy collaboration between humans and collaborative robots (cobots). Standard approaches for Robot-to-hum…
Counterfactual Generative Networks
Axel Sauer, Andreas Geiger
Neural networks are prone to learning shortcuts -- they often model simple correlations, ignoring more complex ones that potentially generalize better. Prior works on image classif…
How to Make Deep RL Work in Practice
Nirnai Rao, Elie Aljalbout, Axel Sauer +1
In recent years, challenging control problems became solvable with deep reinforcement learning (RL). To be able to use RL for large-scale real-world applications, a certain degree…
Tracking Holistic Object Representations
Axel Sauer, Elie Aljalbout, Sami Haddadin
Recent advances in visual tracking are based on siamese feature extractors and template matching. For this category of trackers, latest research focuses on better feature embedding…