论文标题
无监督在gan潜在空间中可解释的方向
Unsupervised Discovery of Interpretable Directions in the GAN Latent Space
论文作者
论文摘要
GAN模型的潜在空间通常具有语义上有意义的方向。在这些方向上移动对应于人类解剖图像变换，例如缩放或重新上色，从而实现了更可控制的生成过程。但是，目前以监督的方式进行此类方向的发现，需要人类标签，预验证的模型或某种形式的自学形式。这些要求严重限制了现有方法可以发现的一系列方向。在本文中，我们介绍了一种无监督的方法，以识别预审计的GAN模型的潜在空间中的可解释方向。通过一个简单的模型 - 不足的程序，我们找到了与明智的语义操作相对应的方向，而没有任何形式的（自我）监督。此外，我们揭示了几个非平凡的发现，这些发现很难通过现有方法获得，例如，与背景去除相对应的方向。作为我们工作的直接实际好处，我们展示了如何利用这一发现以实现竞争性绩效以进行弱监督的显着性检测。
The latent spaces of GAN models often have semantically meaningful directions. Moving in these directions corresponds to human-interpretable image transformations, such as zooming or recoloring, enabling a more controllable generation process. However, the discovery of such directions is currently performed in a supervised manner, requiring human labels, pretrained models, or some form of self-supervision. These requirements severely restrict a range of directions existing approaches can discover. In this paper, we introduce an unsupervised method to identify interpretable directions in the latent space of a pretrained GAN model. By a simple model-agnostic procedure, we find directions corresponding to sensible semantic manipulations without any form of (self-)supervision. Furthermore, we reveal several non-trivial findings, which would be difficult to obtain by existing methods, e.g., a direction corresponding to background removal. As an immediate practical benefit of our work, we show how to exploit this finding to achieve competitive performance for weakly-supervised saliency detection.
