270 Open source projects that are alternatives of or similar to Mzsr

Open Source Image and Video Restoration Toolbox for Super-resolution, Denoise, Deblurring, etc. Currently, it includes EDSR, RCAN, SRResNet, SRGAN, ESRGAN, EDVR, BasicVSR, SwinIR, ECBSR, etc. Also support StyleGAN2, DFDNet.

Codes and datasets for AAAI-2021 paper "Learning to Pre-train Graph Neural Networks"

Bilevel Optimization Library in Python for Multi-Task and Meta Learning

Code repo for "Deep Generative Adversarial Residual Convolutional Networks for Real-World Super-Resolution" (CVPRW NTIRE2020).

NeurIPS'19: Meta-Weight-Net: Learning an Explicit Mapping For Sample Weighting (Pytorch implementation for noisy labels).

Official repository of JSI-GAN (Accepted at AAAI 2020).

The source codes of the paper "Improving Few-shot Text Classification via Pretrained Language Representations" and "When Low Resource NLP Meets Unsupervised Language Model: Meta-pretraining Then Meta-learning for Few-shot Text Classification".

Tensorflow implementation of "Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network" (Ledig et al. 2017)

All materials you need for Federated Learning: blogs, videos, papers, and softwares, etc.

Few-shot binary text classification with Induction Networks and Word2Vec weights initialization

A modular toolbox for meta-learning research with a focus on speed and reproducibility.

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

Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising (TIP, 2017)

Test Time Augmentation (TTA) wrapper for computer vision tasks: segmentation, classification, super-resolution, ... etc.

Trainable models and NN optimization tools

EDSR, RCAN, SRGAN, SRFEAT, ESRGAN

Scale-wise Convolution for Image Restoration

Natural and Realistic Single Image Super-Resolution with Explicit Natural Manifold Discrimination (CVPR, 2019)

A Pytorch implemented Deep SR-ITM (ICCV2019)

Official Pytorch Implementation for Trinity of Pixel Enhancement: a Joint Solution for Demosaicing, Denoising and Super-Resolution

Hierarchical Co-occurrence Network with Prototype Loss for Few-shot Learning (PyTorch)

It is a re-implementation of paper named "Deep Video Super-Resolution Network Using Dynamic Upsampling Filters Without Explicit Motion Compensation" called VSR-DUF model. There are both training codes and test codes about VSR-DUF based tensorflow.

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

Learning to Learn how to Learn: Self-Adaptive Visual Navigation using Meta-Learning (https://arxiv.org/abs/1812.00971)

Deep Plug-and-Play Super-Resolution for Arbitrary Blur Kernels (CVPR, 2019) (PyTorch)

Implementation of the paper "Adversarial Attacks on Graph Neural Networks via Meta Learning".

A list of high-quality (newest) AutoML works and lightweight models including 1.) Neural Architecture Search, 2.) Lightweight Structures, 3.) Model Compression, Quantization and Acceleration, 4.) Hyperparameter Optimization, 5.) Automated Feature Engineering.

Image Super-Resolution Using SRCNN, DRRN, SRGAN, CGAN in Pytorch

A PyTorch implementation of SRGAN based on CVPR 2017 paper "Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network"

[ICLR'19] Meta-learning with differentiable closed-form solvers

A tensorflow implementation of "Fast and Accurate Image Super Resolution by Deep CNN with Skip Connection and Network in Network", a deep learning based Single-Image Super-Resolution (SISR) model.

The code for paper "CANet: Class-Agnostic Segmentation Networks with Iterative Refinement and Attentive Few-Shot Learning"

Fast and Accurate One-Stage Space-Time Video Super-Resolution (accepted in CVPR 2020)

Applying SRGAN technique implemented in https://github.com/zsdonghao/SRGAN on videos to super resolve them.

Official SRFlow training code: Super-Resolution using Normalizing Flow in PyTorch

OpenMMLab Image and Video Editing Toolbox

Deep Unfolding Network for Image Super-Resolution (CVPR, 2020) (PyTorch)

The py version of toflow → https://github.com/anchen1011/toflow

A dataset of datasets for learning to learn from few examples

Awesome GAN for Medical Imaging

Collection for Few-shot Learning

A collection of extensions and data-loaders for few-shot learning & meta-learning in PyTorch

The project is an official implement of our CVPR2018 paper "Deep Back-Projection Networks for Super-Resolution" (Winner of NTIRE2018 and PIRM2018)

A list of recent papers about Meta / few-shot learning methods applied in NLP areas.

Reinforcement Learning Tutorial with Demo: DP (Policy and Value Iteration), Monte Carlo, TD Learning (SARSA, QLearning), Function Approximation, Policy Gradient, DQN, Imitation, Meta Learning, Papers, Courses, etc..

torch implementation of srgan

A Fast Deep Learning Model to Upsample Low Resolution Videos to High Resolution at 30fps

Image Upscaling in Javascript. Increase image resolution up to 4x using Tensorflow.js.

Meta-Learning with Differentiable Convex Optimization (CVPR 2019 Oral)

Official implementation: Intermediate Layer Optimization for Inverse Problems using Deep Generative Models

Tensorflow implementation of pixel-recursive-super-resolution(Google Brain paper: https://arxiv.org/abs/1702.00783)

[Params: Only 272K!!!] Efficient Image Super-Resolution Using Pixel Attention, in ECCV Workshop, 2020.

Memory efficient MAML using gradient checkpointing

ESRGAN (Enhanced Super-Resolution Generative Adversarial Networks, published in ECCV 2018) implemented in Tensorflow 2.0+. This is an unofficial implementation. With Colab.

PyTorch implementation of 1712.06087 "Zero-Shot" Super-Resolution using Deep Internal Learning

GPU-accelerated Levenberg-Marquardt curve fitting in CUDA

Plug-and-Play Image Restoration with Deep Denoiser Prior (PyTorch)

CVPR2019 (oral) Modulating Image Restoration with Continual Levels via Adaptive Feature Modification Layers (AdaFM). PyTorch implementation

Densely Residual Laplacian Super-resolution, IEEE Pattern Analysis and Machine Intelligence (TPAMI), 2020