论文标题
发现多个认知图像属性的多个方向
Discovering Multiple and Diverse Directions for Cognitive Image Properties
论文作者
论文摘要
最近的研究表明，可以在预训练的gan的潜在空间中找到可解释的方向。这些方向可控制并支持各种语义编辑操作。虽然先前的工作集中在发现执行所需编辑操作（例如Zoom-In）的单个方向上，但在发现可以实现所需编辑的多个和不同方向上完成了有限的工作。在这项工作中，我们提出了一个新颖的框架，该框架为特定的感兴趣的特性发现了多个和不同的方向。特别是，我们专注于操纵认知特性，例如记忆性，情感价和美学。我们通过广泛的实验表明，我们的方法成功地操纵了这些特性，同时产生了不同的产出。我们的项目页面和源代码可在http://catlab-team.github.io/latentcognitive上找到。
Recent research has shown that it is possible to find interpretable directions in the latent spaces of pre-trained GANs. These directions enable controllable generation and support a variety of semantic editing operations. While previous work has focused on discovering a single direction that performs a desired editing operation such as zoom-in, limited work has been done on the discovery of multiple and diverse directions that can achieve the desired edit. In this work, we propose a novel framework that discovers multiple and diverse directions for a given property of interest. In particular, we focus on the manipulation of cognitive properties such as Memorability, Emotional Valence and Aesthetics. We show with extensive experiments that our method successfully manipulates these properties while producing diverse outputs. Our project page and source code can be found at http://catlab-team.github.io/latentcognitive.
