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Face Depixelizer работает на основе репозитория “PULSE: Self-Supervised Photo Upsampling via Latent Space Exploration of Generative Models”.

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List of PhD students by supervisor. Below is a list of finished and current PhD students admitted by the Computer Science Degree Committee, sorted by supervisor. Note: If a Computer Laboratory research student's PhD is not listed here, then it might have been dealt with by the Degree Committee for the Faculty of Mathematics (this includes any pre-1987 Computer Laboratory PhD) or by the ...

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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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from keras.models import Model from keras.layers import Input, Conv2D, MaxPooling2D, UpSampling2D, concatenate, Conv2DTranspose, BatchNormalization, Dropout from keras.optimizers import Adam from keras.utils import plot_model from keras import backend as K.

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Recently, Face-Depixelizer, a model based on “PULSE: Self-Supervised photo upsampling via latent space exploration of generative models” has been released. Such model can output the original picture from a pixelised version of it. More rigorously, it would output the closest known depixelised picture.

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PGen supports generative planning with complex processes via three main features. First, PGen goal plans and activity models are encoded using Reactive Model-based Programming Language (RMPL). Second, PGen represents goal plans, plan operators and plan candidates with a uniform representation called Temporal Plan Networks (TPN).

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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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61. pulse-master Sherry_Y 在 2020-10-07 21:27:15 上传 说明:论文《PULSE: Self-Supervised Photo Upsampling via Latent Space Exploration of Generative Models》源代码
This is PULSE, A Self-Supervised Photo Upsampling AI That Can Unblur Photos In the modern days of technology, almost everyone in the world have smartphones. With those devices, people can capture almost anything as soon as it happens.
La technologie baptisée PULSE pour Self-Supervised Photo Upsampling via Latent Space Exploration of Generative Models (suréchantillonnage de photos autosupervisé via l’exploration en espace latent de modèles génératifs, en français) permet d’améliorer considérablement la résolution d’une image.
Aug 10, 2015 · Numerous models have been proposed for modeling social networks to explore their structure or to address application problems, such as community detection and behavior prediction.
Aug 17, 2018 · PROCEEDINGS VOLUME 10806 Tenth International Conference on Digital Image Processing (ICDIP 2018)

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大数据文摘授权转载自Python专栏 一项技术刚开始出现的时候,一般都是很正经的; 一项技术从实验室走入“寻常百姓家 ...
Jun 15, 2020 · The researchers’ AI, dubbed PULSE (Self-Supervised Photo Upsampling via Latent Space Exploration of Generative Models), can generate photorealistic images of faces that are 64 times the resolution of the source image. For instance, a heavily pixelated 16×16-pixel image of a face can be converted into a 1024 x 1024 pixel image….