SITP2018's repositories
vq-vae-2-pytorch
Implementation of Generating Diverse High-Fidelity Images with VQ-VAE-2 in PyTorch
BasicSR
Basic Super-Resolution Toolbox, including SRResNet, SRGAN, ESRGAN, etc.
CLRIQA
Controllable List-wise Ranking for Universal No-reference Image Quality Assessment
cnn_graph
Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering
compare_gan
Compare GAN code.
deep_gcns_torch
Pytorch Repo for "DeepGCNs: Can GCNs Go as Deep as CNNs?" ICCV2019 Oral https://www.deepgcns.org
DigitalImageProcessing
数字图像处理中常用算法实现
DRN
Closed-loop Matters: Dual Regression Networks for Single Image Super-Resolution
GAT
Graph Attention Networks (https://arxiv.org/abs/1710.10903)
gcn
Implementation of Graph Convolutional Networks in TensorFlow
GraphNeuralNetwork
《深入浅出图神经网络：GNN原理解析》配套代码
lpips-tensorflow
Tensorflow port for the Learned Perceptual Image Patch Similarity (LPIPS) metric.
ML-GCN
PyTorch implementation of Multi-Label Image Recognition with Graph Convolutional Networks, CVPR 2019.
NRVQA
no reference image/video quaity assessment(BRISQUE/NIQE/PIQE/DIQA/deepBIQ/VSFA
openwifi
open-source IEEE802.11/Wi-Fi baseband chip/FPGA design
PerceptualImageError
A metric for Perceptual Image-Error Assessment through Pairwise Preference (PieAPP at CVPR 2018).
PerceptualSimilarity
Learned Perceptual Image Patch Similarity (LPIPS) metric. In CVPR, 2018.
planetoid
Semi-supervised learning with graph embeddings
pygcn
Graph Convolutional Networks in PyTorch
pytorch-msssim
PyTorch differentiable Multi-Scale Structural Similarity (MS-SSIM) loss
sgas
SGAS: Sequential Greedy Architecture Search (CVPR'2020) https://www.deepgcns.org/auto/sgas
Single-Image-Super-Resolution
A collection of high-impact and state-of-the-art SR methods
slic-python-implementation
🖼The python implementation to make superpixels by slic.
SPSR
Pytorch implementation of Structure-Preserving Super Resolution with Gradient Guidance (CVPR 2020)
srgan
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
superpixel-benchmark
An extensive evaluation and comparison of 28 state-of-the-art superpixel algorithms on 5 datasets.