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
Photographic Image Synthesis with Cascaded Refinement Networks - Pytorch Implementation
Implementation of style transfer by tensorflow, for detail please see the paper "Image Style Transfer Using Convolutional Neural Networks"(CVPR2016)
Tensorflow implementation of "Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network" (Ledig et al. 2017)
Pre-trained VGG-Net Model for image classification using tensorflow
Implementation of the paper : Deep image analogy
Transferring the style of one image to the contents of another image, using PyTorch and VGG19.
Fast and Accurate User constrained Thumbnail Generation using Adaptive Convolutions. | ICASSP 2019 [ORAL]
Optimal deep texture generation and style transfer based on Eric Risser's paper
Multi-Sensor Image (infrared and visible) Fusion using deep learning framework, Principal Component Analysis, Discrete Wavelet Transform
This code mainly implement the paper ' Arbitrary Style Transfer in Real-time with Adaptive Instance Normalization ' by TensorFlow
A Multi-modal Framework for Sentimental Analysis of Meme
Repository containing scripts to train and test a neural network whose goal is to detect presence of COVID-19
Implement lenet and vgg19 by tensorflow with dataset mnist using tfrecord
My PyTorch implementation of CNNs. All networks in this repository are using CIFAR-100 dataset for training.
Image classification models on CIFAR10 dataset using pytorch
📜 A Novel Facial Emotion Recognition Model Using Segmentation VGG-19 Architecture
PyTorch version of the paper: "Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network"
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