Fast-SRGAN VS kervolution

Compare Fast-SRGAN vs kervolution and see what are their differences.

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Fast-SRGAN kervolution
- 1
642 38
- -
0.0 0.0
2 months ago almost 4 years ago
Python Python
MIT License GNU General Public License v3.0 only
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Fast-SRGAN

Posts with mentions or reviews of Fast-SRGAN. We have used some of these posts to build our list of alternatives and similar projects.

We haven't tracked posts mentioning Fast-SRGAN yet.
Tracking mentions began in Dec 2020.

kervolution

Posts with mentions or reviews of kervolution. We have used some of these posts to build our list of alternatives and similar projects.

What are some alternatives?

When comparing Fast-SRGAN and kervolution you can also consider the following projects:

a-PyTorch-Tutorial-to-Super-Resolution - Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network | a PyTorch Tutorial to Super-Resolution

quaterion - Blazing fast framework for fine-tuning similarity learning models

awesome-colab-notebooks - Collection of google colaboratory notebooks for fast and easy experiments

iSeeBetter - 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

super-resolution - Tensorflow 2.x based implementation of EDSR, WDSR and SRGAN for single image super-resolution

CleanTF2plus - Clean TF2's sequel

EDSR_Tensorflow - TensorFlow implementation of 'Enhanced Deep Residual Networks for Single Image Super-Resolution'.

EGVSR - Efficient & Generic Video Super-Resolution

co-mod-gan - [ICLR 2021, Spotlight] Large Scale Image Completion via Co-Modulated Generative Adversarial Networks

data-efficient-gans - [NeurIPS 2020] Differentiable Augmentation for Data-Efficient GAN Training

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

SinGAN - Official pytorch implementation of the paper: "SinGAN: Learning a Generative Model from a Single Natural Image"