content-loss

Here are 9 public repositories matching this topic...

braindotai / Real-Time-Super-Resolution

🔥 Real-time Super Resolution enhancement (4x) with content loss and relativistic adversarial optimization 🔥

generator tensorflow discriminator vggnet super-resolution srgan image-enhancement relativistic-discriminator ragan relativistic-gan esrgan content-loss efficientnet div2k style-loss perceptual-loss

Updated Jul 27, 2021
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ShawnNew / AMMNet

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A Tri-map free direct Alpha Matting.

attention multi-scale adversarial-loss alpha-matting content-loss

Updated Sep 21, 2019
Python

souravs17031999 / Neuralstyletransfer-showcase

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This is a social volunteering project for implementing neural style transfer algorithm for creating artistic styles with awesome textures to be printed on tees :)

udacity deep-learning neural-network cnn style-transfer charity convolutional-neural-networks volunteering social-cause neural-style-transfer style-transfer-algorithms content-loss teespring secure-and-private-ai spaic style-loss neural-style-transfer-pytorch

Updated Oct 24, 2020
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rjnp2 / neural-style-transfer

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tensorflow vgg-16 neural-style-transfer content-loss style-loss-weights

Updated Mar 5, 2021
Jupyter Notebook

sharmasapna / artistic-style-transfer-with-neural-net

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This is python code for the implementation of the paper a Neural Algorithm for Artistic Style

python machine-learning deep-learning neural-network tensorflow pytorch style-transfer convolutional-neural-networks vgg19 content-loss tensorflow1 gram-matrix style-loss

Updated Sep 11, 2021
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EdoardoGruppi / AMLS_II_assignment20_21

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This project is aimed to develop powerful and lightweight networks to address the two tasks presented in the NTIRE 2017 SuperResolution Challenge. The two deep learning models proposed, i.e. a CNN and a GAN, achieved satisfactory results demonstrating an important ability in introducing high-frequency information to the original low resolution i…

computer-vision deep-learning generative-adversarial-network convolutional-neural-networks super-resolution single-image-super-resolution content-loss