Learning meaningful and compact representations with structurally 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 450'000 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. In addition, we provide two more datasets which consist of simulations of the experimental setup. These datasets provide for the first time the possibility to systematically investigate how well different disentanglement methods perform on real data in comparison to simulation, and how simulated data can be leveraged to build better representations of the real world.

本文提出了一个包含超过一百万张3D物体图像的数据集，以及在实验平台上运用机械臂移动物体的方式，以此来精确控制所有变化的因素，实现了不同解缠绕建模方法对模拟数据和真实数据的比对，研究结果表明模型选择是从模拟数据向真实数据传递信息的有效手段。

从模拟到现实的感性偏见转移：一个新的解缠数据集