Pinned Repositories
Semi-supervised-learning
A Unified Semi-Supervised Learning Codebase (NeurIPS'22)
annotated_deep_learning_paper_implementations
🧑🏫 59 Implementations/tutorials of deep learning papers with side-by-side notes 📝; including transformers (original, xl, switch, feedback, vit, ...), optimizers (adam, adabelief, ...), gans(cyclegan, stylegan2, ...), 🎮 reinforcement learning (ppo, dqn), capsnet, distillation, ... 🧠
CABM-pytorch
CABM: Content-Aware Bit Mapping for Single Image Super-Resolution Network with Large Input
DADIP-pytorch
Blind Image Deblurring Based on Dual Attention Network and 2D Blur Kernel Estimation
deep-image-prior
Image restoration with neural networks but without learning.
Low-Level-Vision-Paper-Record
记录近期的 1) 图像/视频的超分增强等low level vision任务; 2) 图像生成 等任务相关论文, 主要为18年以后的DL based方法.
openHEVC_feature_decoder
pytorch_misc
Code snippets created for the PyTorch discussion board
Sheldon04
Sheldon04.github.io
Github Pages template for academic personal websites, forked from mmistakes/minimal-mistakes
Sheldon04's Repositories
Sheldon04/CABM-pytorch
CABM: Content-Aware Bit Mapping for Single Image Super-Resolution Network with Large Input
Sheldon04/DADIP-pytorch
Blind Image Deblurring Based on Dual Attention Network and 2D Blur Kernel Estimation
Sheldon04/annotated_deep_learning_paper_implementations
🧑🏫 59 Implementations/tutorials of deep learning papers with side-by-side notes 📝; including transformers (original, xl, switch, feedback, vit, ...), optimizers (adam, adabelief, ...), gans(cyclegan, stylegan2, ...), 🎮 reinforcement learning (ppo, dqn), capsnet, distillation, ... 🧠
Sheldon04/deep-image-prior
Image restoration with neural networks but without learning.
Sheldon04/Low-Level-Vision-Paper-Record
记录近期的 1) 图像/视频的超分增强等low level vision任务; 2) 图像生成 等任务相关论文, 主要为18年以后的DL based方法.
Sheldon04/openHEVC_feature_decoder
Sheldon04/pytorch_misc
Code snippets created for the PyTorch discussion board
Sheldon04/Sheldon04
Sheldon04/Sheldon04.github.io
Github Pages template for academic personal websites, forked from mmistakes/minimal-mistakes