Back
The Max Planck Institute for Intelligent Systems, GoogleAI, and MILA Montreal have launched the Disentanglement Challenge. Registration is now open: sign up now and bring disentangled representations to the REAL world - with 20,000 euros in prize money and best paper awards!
The success of machine learning algorithms depends heavily on the representation of the data. It is widely believed that good representations are distributed, invariant and disentangled. This challenge focuses on disentangled representations where explanatory factors of the data tend to change independently of each other.
Given the growing importance of the field and the potential societal impact in the medical domain or fair decision making, it is high time to bring disentanglement to the real world:
Stage 1: Sim-to-real transfer learning - design representation learning algorithms on simulated data and transfer them to the real world.
Stage 2: Advancing disentangled representation learning to complicated physical objects.
More information