Jinglong Du's repositories
modulated-deform-conv
deformable convolution 2D 3D DeformableConvolution DeformConv Modulated Pytorch CUDA
EDSR-PyTorch
PyTorch version of the paper 'Enhanced Deep Residual Networks for Single Image Super-Resolution' (CVPRW 2017)
keras-inception-resnet-v2
The Inception-ResNet v2 model using Keras (with weight files)
CBAM.PyTorch
Non-official implement of Paper:CBAM: Convolutional Block Attention Module
berkeley-stat-157
Homepage for STAT 157 at UC Berkeley
SeaNet-PyTorch
This repository is a PyTorch version of "Soft-edge Assisted Network for Single Image Super-Resolution". (IEEE TIP 2020)
deeplearning-assignment
深度学习笔记
dlcv_for_beginners
《深度学习与计算机视觉》配套代码
LAM_Demo
Demo package for colab tutorial
RFANet
Residual Feature Aggregation Network for Image Super-Resolution
DBPN-Pytorch
The project is an official implement of our CVPR2018 paper "Deep Back-Projection Networks for Super-Resolution" (Winner of NTIRE2018 and PIRM2018)
mri-deep-learning-tools
Resurces for MRI images processing and deep learning in 3D
SAN
Exploring Self-attention for Image Recognition, CVPR2020.
Pyramid-Attention-Networks
[IJCV] Pyramid Attention Networks for Image Restoration: new SOTA results on multiple image restoration tasks: denoising, demosaicing, compression artifact reduction, super-resolution
MZSR
Meta-Transfer Learning for Zero-Shot Super-Resolution (CVPR, 2020)
SAN-1
Second-order Attention Network for Single Image Super-resolution (CVPR-2019)
CBAM
CBAM: Convolutional Block Attention Module for CIFAR10 on ResNet backbone with Pytorch
attention-module
Official PyTorch code for "BAM: Bottleneck Attention Module (BMVC2018)" and "CBAM: Convolutional Block Attention Module (ECCV2018)"
Brain-MRI-Axial-T1-Image-SRCNN-
Using Three Layers SRCNN (Proposed by Chao Dong at all) to Brain MRI Axial T1 Image. I programmed with Python tensorflow. Training the model in Google colab.
pytorch-deform-conv-v2
PyTorch implementation of Deformable ConvNets v2 (Modulated Deformable Convolution)
Single-Image-Super-Resolution
A collection of high-impact and state-of-the-art SR methods
reproducible-image-denoising-state-of-the-art
Collection of popular and reproducible image denoising works.
super-resolution-1
collection of super-resolution models & algorithms
PixelShuffle3D
implement of PixelShuffle3D module with pytorch
DeepLearning-500-questions
深度学习500问,以问答形式对常用的概率知识、线性代数、机器学习、深度学习、计算机视觉等热点问题进行阐述,以帮助自己及有需要的读者。 全书分为15个章节,近20万字。由于水平有限,书中不妥之处恳请广大读者批评指正。 未完待续............ 如有意合作,联系scutjy2015@163.com 版权所有,违权必究 Tan 2018.06