论文标题
部分可观测时空混沌系统的无模型预测
Exploring the Effectiveness of Mask-Guided Feature Modulation as a Mechanism for Localized Style Editing of Real Images
论文作者
论文摘要
高分辨率图像生成的深层生成模型的成功导致它们广泛利用了真实图像的样式编辑。大多数现有方法都依靠将真实图像倒在其潜在空间的原理上，然后确定可控的方向。真实图像的反转和确定可控的潜在方向都是计算昂贵的操作。此外，确定可控的潜在方向需要额外的人类监督。这项工作旨在探索深层生成模型的潜在空间中蒙版特征调制的功效，以解决这些瓶颈。为此，我们介绍了Smanticantyle AutoCoder（SSAE），这是一种深层生成的自动编码器模型，该模型利用语义掩码引导的潜在空间操纵来进行真实图像的高度局部化学现实主义样式编辑。我们为此提供了定性和定量结果及其分析。这项工作应作为未来工作的指导入门。
The success of Deep Generative Models at high-resolution image generation has led to their extensive utilization for style editing of real images. Most existing methods work on the principle of inverting real images onto their latent space, followed by determining controllable directions. Both inversion of real images and determination of controllable latent directions are computationally expensive operations. Moreover, the determination of controllable latent directions requires additional human supervision. This work aims to explore the efficacy of mask-guided feature modulation in the latent space of a Deep Generative Model as a solution to these bottlenecks. To this end, we present the SemanticStyle Autoencoder (SSAE), a deep Generative Autoencoder model that leverages semantic mask-guided latent space manipulation for highly localized photorealistic style editing of real images. We present qualitative and quantitative results for the same and their analysis. This work shall serve as a guiding primer for future work.
