On the Transfer of Inductive Bias from Simulation to the Real World: a New Disentanglement Dataset

Understand

Learning meaningful and compact representations with disentangled semantic aspects is considered to be of key importance in representation learning.

• Since real-world data is notoriously costly to collect, many recent state-of-the-art disentanglement models have heavily relied on synthetic toy data-sets.
• In this paper, we propose a novel data-set which consists of over one million images of physical 3D objects with seven factors of variation, such as object color, shape, size and position.
• In order to be able to control all the factors of variation precisely, we built an experimental platform where the objects are being moved by a robotic arm.