Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
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Updated
Feb 22, 2024 - Python
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
Tensorflow implementation of the SRGAN algorithm for single image super-resolution
ImageNet pre-trained models with batch normalization for the Caffe framework
Pre-trained VGG-Net Model for image classification using tensorflow
Tensorflow implementation of "Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network" (Ledig et al. 2017)
Implementation of style transfer by tensorflow, for detail please see the paper "Image Style Transfer Using Convolutional Neural Networks"(CVPR2016)
Photographic Image Synthesis with Cascaded Refinement Networks - Pytorch Implementation
Implementation of the paper : Deep image analogy
Fast and Accurate User constrained Thumbnail Generation using Adaptive Convolutions. | ICASSP 2019 [ORAL]
神经风格迁移——基于keras实现(VGG19)2019-2-23
This code mainly implement the paper ' Arbitrary Style Transfer in Real-time with Adaptive Instance Normalization ' by TensorFlow
Repository containing scripts to train and test a neural network whose goal is to detect presence of COVID-19
This repo is dedicated to the medical reserach for skin and breast cancer and brain tumor detection detection by using NN and SVM and vgg19
PyTorch version of the paper: "Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network"
Transferring the style of one image to the contents of another image, using PyTorch and VGG19.
A Multi-modal Framework for Sentimental Analysis of Meme
Generate novel artistic images using neural style transfer algorithm
Multi-Sensor Image (infrared and visible) Fusion using deep learning framework, Principal Component Analysis, Discrete Wavelet Transform
Deep Learning course final project. 12th semester.
Graduation Project. Applying Generative Adversarial Networks(GAN) with Residual-In-Residual(RIR) blocks.
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