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
VggML (ICPR 2018, Beijing)
A program that can add an artistic touch to any image.
VGG16 Net implementation from PyTorch Examples scripts for ImageNet dataset
Multiclass image classification using Convolutional Neural Network
Neural Style implementation in PyTorch! 🎨
Photographic Image Synthesis with Cascaded Refinement Networks - Pytorch Implementation
Generative Adversarial Network for single image super-resolution in high content screening microscopy images
Implementation of style transfer by tensorflow, for detail please see the paper "Image Style Transfer Using Convolutional Neural Networks"(CVPR2016)
The VGG16 and VGG19 networks in caffe with jupyter notebook
Transferring the style of one image to the contents of another image, using PyTorch and VGG19.
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
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
Deepdream experiment implemented using Keras and VGG19 convnet
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