追求美：将图像标签转换为有意义的矢量
The pursuit of beauty: Converting image labels to meaningful vectors
摘要：
计算机视觉社区的一个挑战是理解图像的语义，以便允许基于现有的高级特征进行图像重建或更好地分析（半）标记数据集。为了应对这一挑战，本文介绍了一种方法，称为基于遮挡的潜在表示（OLR），用于将图像标签转换为有意义的表示，捕获大量的数据语义。除了信息丰富之外，这些表示还组成了一个解纠缠的低维潜在空间，其中每个图像标签都被编码到一个单独的向量中。我们在一系列实验中评估了这些表示的质量，其结果表明，该模型可以捕获数据概念和发现数据的相互关系。
英文摘要：
A challenge of the computer vision community is to understand the semantics of an image, in order to allow image reconstruction based on existing high-level features or to better analyze (semi-)labelled datasets. Towards addressing this challenge, this paper introduces a method, called Occlusion-based Latent Representations (OLR), for converting image labels to meaningful representations that capture a significant amount of data semantics. Besides being informational rich, these representations compose a disentangled low-dimensional latent space where each image label is encoded into a separate vector. We evaluate the quality of these representations in a series of experiments whose results suggest that the proposed model can capture data concepts and discover data interrelations.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
