Learning disentangled representations of data is a fundamental problem in artificial intelligence. Specifically, disentangled latent representations allow generative models to control and compose the disentangled factors in the synthesis process. Current methods, however, require extensive supervision and training, or instead, noticeably compromise quality. In this paper, we present a method that learns how to represent data in a disentangled way, with minimal supervision, manifested solely using available pre-trained networks. Our key insight is to decouple the processes of disentanglement and synthesis, by employing a leading pre-trained unconditional image generator, such as StyleGAN. By learning to map into its latent space, we leverage both its state-of-the-art quality, and its rich and expressive latent space, without the burden of training it. We demonstrate our approach on the complex and high dimensional domain of human heads. We evaluate our method qualitatively and quantitatively, and exhibit its success with de-identification operations and with temporal identity coherency in image sequences. Through extensive experimentation, we show that our method successfully disentangles identity from other facial attributes, surpassing existing methods, even though they require more training and supervision.

中文翻译：

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通过潜在空间映射解开人脸身份

学习数据的解耦表示是人工智能中的一个基本问题。具体来说，分离的潜在表示允许生成模型在合成过程中控制和组合分离的因素。然而，当前的方法需要大量的监督和培训，或者相反，会明显影响质量。在本文中，我们提出了一种方法，该方法学习如何以一种解开的方式表示数据，只需最少的监督，仅使用可用的预训练网络即可体现。我们的主要见解是通过采用领先的预训练无条件图像生成器（例如 StyleGAN）来解耦解缠结和合成过程。通过学习映射到它的潜在空间，我们利用了它最先进的质量，以及它丰富而富有表现力的潜在空间，没有训练它的负担。我们展示了我们在人类头部复杂和高维领域的方法。我们定性和定量地评估我们的方法，并展示其在去识别操作和图像序列中的时间一致性方面的成功。通过广泛的实验，我们表明我们的方法成功地将身份与其他面部属性分开，超越了现有方法，尽管它们需要更多的培训和监督。