Pulse_ self supervised photo upsampling via latent space exploration of generative models

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  • The tool is based on the “PULSE: Self-Supervised Photo Upsampling via Latent Space Exploration of Generative Models” study. The way the algorithm works is the AI gets a pixelated image, which it compares to a bunch of proper quality portraits that it also pixelates down to the necessary quality to find the one that looks the most identical ...
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    Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network摘要论文主体实现代码 CVPR2020-图像重建相关论文整理1. PULSE: Self-Supervised Photo Upsampling via Latent Space Exploration of Generative Models参考 1. PULSE: Self-Supervised...

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    DOI: 10.1109/cvpr42600.2020.00251 Corpus ID: 212634162. PULSE: Self-Supervised Photo Upsampling via Latent Space Exploration of Generative Models @article{Menon2020PULSESP, title={PULSE: Self-Supervised Photo Upsampling via Latent Space Exploration of Generative Models}, author={Sachit Menon and A. Damian and S. Hu and Nikhil Ravi and C. Rudin}, journal={2020 IEEE/CVF Conference on Computer ...

    PULSE: Self-Supervised Photo Upsampling via Latent Space Exploration of Generative Models Paper Submitted by dmonn | 5 months ago 1 DeepFakes and Beyond: A Survey of Face Manipulation and Fake Detection Paper Submitted by dmonn | 10 months ago 1

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    这项研究于本月在计算机视觉与模式识别顶会CVPR 2020上发表,论文标题为《PULSE:通过对生成模型的潜在空间探索实现自监督照片上采样(PULSE:Self-Supervised Photo Upsampling via Latent Space Exploration of Generative Models)》。

    Face-Depixelizer. Face Depixelizer based on "PULSE: Self-Supervised Photo Upsampling via Latent Space Exploration of Generative Models" repository. Given a low-resolution input image, Face Depixelizer searches the outputs of a generative model (here, StyleGAN) for high-resolution images that are perceptually realistic and downscale correctly.

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    Mar 08, 2020 · PULSE: Self-Supervised Photo Upsampling via Latent Space Exploration of Generative Models. The primary aim of single-image super-resolution is to construct high-resolution (HR) images from corresponding low-resolution (LR) inputs. In previous approaches, which have generally been supervised, the training objective typically measures a pixel-wise average distance between the super-resolved (SR) and HR images.

    PULSE: Self-Supervised Photo Upsampling via Latent Space Exploration of Generative Models [4] Unsupervised Translation of Programming Languages [5] PIFuHD: Multi-Level Pixel-Aligned Implicit Function for High-Resolution 3D Human Digitization [6] High-Resolution Neural Face Swapping for Visual Effects [7]

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lem, PULSE (Photo Upsampling via Latent Space Explo-ration), which generates high-resolution, realistic images at resolutions previously unseen in the literature. It ac-complishes this in an entirely self-supervised fashion and is not confined to a specific degradation operator used during training, unlike previous methods (which require training
Nov 13, 2020 · Computer vision is interesting, huh? Have you seen the demonstrations of Pulse [1] or NVIDIA Maxine [2]? However, it’s common when there are no trained models for your task, no benchmark datasets, no easy to follow tutorials, and you don’t have a whole team to develop wonderful machine learning models.