jinglongdu Goto Github PK
Name: Jinglong Du
Type: User
Company: Chonqing University
Name: Jinglong Du
Type: User
Company: Chonqing University
Github Pages template for academic personal websites, forked from mmistakes/minimal-mistakes
The implementation of "Enriched CNN-Transformer Feature Aggregation Networks for Super-Resolution" [WACV 2023].
Official Pytorch Code for "Rethinking Degradation: Radiograph Super-Resolution via AID-SRGAN" - MICCAI 2022 Workshop
Extended Local Attribution Maps from SISR Demo to Video Demo
Advanced Normalization Tools (ANTs)
[peer review] An Arbitrary Scale Super-Resolution Approach for 3D MR Images using Implicit Neural Representation
Self-attention、Non-local、SE、SK、CBAM、DANet
Official PyTorch code for "BAM: Bottleneck Attention Module (BMVC2018)" and "CBAM: Convolutional Block Attention Module (ECCV2018)"
Homepage for STAT 157 at UC Berkeley
BioTIP: Biological Tipping-Point Identification and Characterization
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.
A pytorch implement of CANM_Net.
PyTorch code for our NeurIPS 2022 paper "Cross Aggregation Transformer for Image Restoration"
CBAM: Convolutional Block Attention Module for CIFAR10 on ResNet backbone with Pytorch
Non-official implement of Paper:CBAM: Convolutional Block Attention Module
MRI super-resolution reconstruction using multi-level Conv-Deconv network.
使用 express 和 vue3 搭建的 ChartGPT 演示网页
✔️最全面的 深度学习CV 笔记【吴恩达 深度学习】【李沐 动手学深度学习】【我是土堆 Pytorch】
CVPR 2022 论文和开源项目合集
《动手学深度学习》:面向中文读者、能运行、可讨论。中英文版被60个国家的400所大学用于教学。
PyTorch code for our ICCV 2023 paper "Dual Aggregation Transformer for Image Super-Resolution"
The project is an official implement of our CVPR2018 paper "Deep Back-Projection Networks for Super-Resolution" (Winner of NTIRE2018 and PIRM2018)
MRI super-resolution reconstruction method: DDSR and DDSR+.
深度学习入门教程, 优秀文章, Deep Learning Tutorial
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[email protected] 版权所有,违权必究 Tan 2018.06
深度学习笔记
本项目将《动手学深度学习》(Dive into Deep Learning)原书中的MXNet实现改为PyTorch实现。
《深度学习与计算机视觉》配套代码
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