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

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pulseは16×16ピクセルの画像を数秒で1024×1024ピクセルに変換し、元がモザイク画像だったとは思えないほど非常に高解像度の画像に仕上げることが可能。このため、毛穴やしわ、髪の1本1本まで描写できるようになっています。

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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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    Self-supervised learning opens up a huge opportunity for better utilizing unlabelled data, while learning in a supervised learning manner. This post covers many interesting ideas of self-supervised learning tasks on images, videos, and control problems.

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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.