Self-supervised learning is a promising direction that aims to learn representations from the data itself without explicit and potentially even manual supervision. One of the major benefits of self-supervised learning is the ability to scale to large amounts of unlabelled data in a lifelong learning manner and to improve performance by reducing the effect of dataset bias. Recent development in self-supervised learning has resulted in achieving comparable or better performance than fully-supervised models. However, many of these methods are developed in domain-specific communities such as robotics, computer vision or reinforcement learning. The aim of this workshop is to bring together researchers from different communities to discuss opportunities, challenges and explore new directions.
Pieter Abbeel (UC Berkeley & Covariant.AI)
Dieter Fox (University of Washington & NVIDIA)
Abhinav Gupta (CMU & Facebook AI Research)
Roberto Calandra (Facebook AI Research)
Chelsea Finn (Stanford University)
Pierre Sermanet (Google Brain)
Andy Zeng (Google Brain)
The focus topics of our workshop include, but are not restricted to:
March 12, 2020 Call for Contributions
April 9, 2020 Submission Deadline
April 30, 2020 Author Notification
May 15, 2020 Camera Ready Submission
July 13, 2020 Workshop
Abhinav Valada (University of Freiburg)< valada(at)cs.uni-freiburg.de > (Primary contact)
Anelia Angelova (Google Research/Google Brain) < anelia(at)google.com >
Joschka Boedecker (University of Freiburg) < jboedeck(at)informatik.uni-freiburg.de >
Oier Mees (University of Freiburg) < meeso(at)informatik.uni-freiburg.de >
Wolfram Burgard (University of Freiburg) < burgard(at)informatik.uni-freiburg.de >