1654 Open source projects that are alternatives of or similar to Fast Srgan

iSeeBetter: Spatio-Temporal Video Super Resolution using Recurrent-Generative Back-Projection Networks | Python3 | PyTorch | GANs | CNNs | ResNets | RNNs | Published in Springer Journal of Computational Visual Media, September 2020, Tsinghua University Press

Deep Learning Super Sampling with Deep Convolutional Generative Adversarial Networks.

Tensorflow implementation of the SRGAN algorithm for single image super-resolution

PyTorch implementation of GANs (Generative Adversarial Networks). DCGAN, Pix2Pix, CycleGAN, SRGAN

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Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network

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A Pytorch implementation of Stylegan2 with UNet Discriminator

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Simplest working implementation of Stylegan2, state of the art generative adversarial network, in Pytorch. Enabling everyone to experience disentanglement

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A DCGAN to generate anime faces using custom mined dataset

TensorFlow template application for deep learning

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👔👗🕶️🎩 Cool resources about Fashion + AI! (papers, datasets, workshops, companies, ...) (constantly updating)

Programming assignments and quizzes from all courses within the GANs specialization offered by deeplearning.ai

Enhanced SRGAN. Champion PIRM Challenge on Perceptual Super-Resolution

Generate Cartoon Images using Generative Adversarial Network

🎨 🎨 深度学习 卷积神经网络教程 ：图像识别，目标检测，语义分割，实例分割，人脸识别，神经风格转换，GAN等🎨🎨 https://dataxujing.github.io/CNN-paper2/

Keras (tensorflow) implementation of SincNet (Mirco Ravanelli, Yoshua Bengio - https://github.com/mravanelli/SincNet)

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Code for the paper: Benhenda, M. 2017. ChemGAN challenge for drug discovery: can AI reproduce natural chemical diversity? arXiv preprint arXiv:1708.08227.

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Pytorch implementation of "Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network"

Transfer Learning Suite in Keras. Perform transfer learning using any built-in Keras image classification model easily!

TensorFlow code for ECCV 2018 paper "Image Super-Resolution Using Very Deep Residual Channel Attention Networks"

It contains the coursework and the practice I have done while learning Deep Learning.🚀 👨‍💻💥 🚩🌈

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Heterogeneous Run Time version of Caffe. Added heterogeneous capabilities to the Caffe, uses heterogeneous computing infrastructure framework to speed up Deep Learning on Arm-based heterogeneous embedded platform. It also retains all the features of the original Caffe architecture which users deploy their applications seamlessly.

A denoising autoencoder + adversarial losses and attention mechanisms for face swapping.

CNN visualization tool in TensorFlow

Vanilla GAN implemented on top of keras/tensorflow enabling rapid experimentation & research. Branches correspond to implementations of stable GAN variations (i.e. ACGan, InfoGAN) and other promising variations of GANs like conditional and Wasserstein.

Torch implementation of various types of GAN (e.g. DCGAN, ALI, Context-encoder, DiscoGAN, CycleGAN, EBGAN, LSGAN)

Learning a Single Convolutional Super-Resolution Network for Multiple Degradations (CVPR, 2018) (Matlab)