论文标题
与条件隐式最大似然估计的多模式图像合成
Multimodal Image Synthesis with Conditional Implicit Maximum Likelihood Estimation
论文作者
论文摘要
计算机视觉和图形中的许多任务都属于有条件图像合成的框架。近年来，生成的对抗网（GAN）在合成图像的质量方面取得了令人印象深刻的进步。但是，由于模式崩溃的问题，同一输入同时生成不同的图像和合理图像仍然是一个挑战。在本文中，我们基于隐式最大似然估计（IMLE）开发了一种新的通用多模式条件图像合成方法，并在两个任务上展示了改进的多模式图像合成性能，即来自场景布局的单个图像超分辨率和图像合成。我们公开实施。
Many tasks in computer vision and graphics fall within the framework of conditional image synthesis. In recent years, generative adversarial nets (GANs) have delivered impressive advances in quality of synthesized images. However, it remains a challenge to generate both diverse and plausible images for the same input, due to the problem of mode collapse. In this paper, we develop a new generic multimodal conditional image synthesis method based on Implicit Maximum Likelihood Estimation (IMLE) and demonstrate improved multimodal image synthesis performance on two tasks, single image super-resolution and image synthesis from scene layouts. We make our implementation publicly available.
