论文标题
探索gan潜在几何形状的线性逆问题的解决方案空间
Exploring the solution space of linear inverse problems with GAN latent geometry
论文作者
论文摘要
反问题包括从不完整的测量集重建信号，其性能高度取决于通过正则化编码的先验知识的质量。尽管传统方法着重于获得独特的解决方案，但新兴趋势考虑了探索多种临时解决方案。在本文中，我们提出了一种生成多个重建的方法，该重建既适合测量值，又是由生成对手网络学到的数据驱动的先验。特别是，我们表明，从初始解决方案开始，可以在生成模型的潜在空间中找到对远期操作员无效的方向，从而与测量值保持一致，同时诱发显着的感知变化。我们的探索方法允许为反问题生成多个解决方案，比现有方法快的数量级。我们显示了图像超分辨率和介入问题的结果。
Inverse problems consist in reconstructing signals from incomplete sets of measurements and their performance is highly dependent on the quality of the prior knowledge encoded via regularization. While traditional approaches focus on obtaining a unique solution, an emerging trend considers exploring multiple feasibile solutions. In this paper, we propose a method to generate multiple reconstructions that fit both the measurements and a data-driven prior learned by a generative adversarial network. In particular, we show that, starting from an initial solution, it is possible to find directions in the latent space of the generative model that are null to the forward operator, and thus keep consistency with the measurements, while inducing significant perceptual change. Our exploration approach allows to generate multiple solutions to the inverse problem an order of magnitude faster than existing approaches; we show results on image super-resolution and inpainting problems.
