论文标题
与相对查询的Oracle指导图像合成
Oracle Guided Image Synthesis with Relative Queries
论文作者
论文摘要
以用户友好的方式隔离和控制生成模型输出的特定功能是一个困难而开放的问题。 We develop techniques that allow an oracle user to generate an image they are envisioning in their head by answering a sequence of relative queries of the form \textit{"do you prefer image $a$ or image $b$?"} Our framework consists of a Conditional VAE that uses the collected relative queries to partition the latent space into preference-relevant features and non-preference-relevant features.然后，我们使用用户对相对查询的响应来确定与他们设想的输出图像相对应的优先级功能。此外，我们开发了建模图像预测相关特征中不确定性的技术，从而使我们的框架可以推广到相对查询训练集包含噪声的场景。
Isolating and controlling specific features in the outputs of generative models in a user-friendly way is a difficult and open-ended problem. We develop techniques that allow an oracle user to generate an image they are envisioning in their head by answering a sequence of relative queries of the form \textit{"do you prefer image $a$ or image $b$?"} Our framework consists of a Conditional VAE that uses the collected relative queries to partition the latent space into preference-relevant features and non-preference-relevant features. We then use the user's responses to relative queries to determine the preference-relevant features that correspond to their envisioned output image. Additionally, we develop techniques for modeling the uncertainty in images' predicted preference-relevant features, allowing our framework to generalize to scenarios in which the relative query training set contains noise.
