Developing meaningful and efficient representations that separate the fundamental structure of the data generation mechanism is crucial in representation learning. However, Disentangled Representation Learning has not fully shown its potential on real images, because of correlated generative factors, their resolution and limited access to ground truth labels. Specifically on the latter, we investigate the possibility of leveraging synthetic data to learn general-purpose disentangled representations applicable to real data, discussing the effect of fine-tuning and what properties of disentanglement are preserved after the transfer. We provide an extensive empirical study to address these issues. In addition, we propose a new interpretable intervention-based metric, to measure the quality of factors encoding in the representation. Our results indicate that some level of disentanglement, transferring a representation from synthetic to real data, is possible and effective.

本研究解决了目前解耦表示学习在真实图像上的潜力未得到充分发挥的问题，特别是由于生成因素的相关性和真实标签的有限获取。通过利用合成数据学习通用的解耦表示并进行微调，我们的研究表明，在合成数据与真实数据之间转移代表性是可能且有效的，并提出了一种新的可解释干预式指标来评估代表性中的编码质量。

转移解耦表示：弥合合成图像与真实图像之间的差距