Pinned Repositories
2048-AI
A simple AI for 2048
2048_rl
A reinforcement learning project on 2048
abu
阿布量化交易系统(股票，期权，期货，比特币，机器学习) 基于python的开源量化交易，量化投资架构
Algorithm_Interview_Notes-Chinese
2018/2019/校招/春招/秋招/算法/机器学习(Machine Learning)/深度学习(Deep Learning)/自然语言处理(NLP)/C/C++/Python/面试笔记
Awesome-Denoise
Awesome Denoising papers w/o code
deeplearningbook-chinese
Deep Learning Book Chinese Translation
dlib
A toolkit for making real world machine learning and data analysis applications in C++
lihang_book_algorithm
致力于将李航博士《统计学习方法》一书中所有算法实现一遍
zi2zi
Learning Chinese Character style with conditional GAN
tjussh's Repositories
tjussh/AdderNet
Code for paper " AdderNet: Do We Really Need Multiplications in Deep Learning?"
tjussh/ADNet
Pytorch implementation of ADNet. (The winning method of the first edition of NTIRE2021 Multi-Frame HDR Challenge)
tjussh/ArbSR
[ICCV 2021] Learning A Single Network for Scale-Arbitrary Super-Resolution
tjussh/bpn
tjussh/CF-Net
Official repository of "Deep Coupled Feedback Network for Joint Exposure Fusion and Image Super-Resolution"
tjussh/Component-Divide-and-Conquer-for-Real-World-Image-Super-Resolution
tjussh/d2l-zh
《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多个国家的400多所大学用于教学。
tjussh/DCNv2
Deformable Convolutional Networks v2 with Pytorch
tjussh/DeepFaceLive
tjussh/DeepHDRVideo
HDR Video Reconstruction: A Coarse-to-fine Network and A Real-world Benchmark Dataset (ICCV 2021)
tjussh/fast-openISP
fast-openISP: a faster re-implementation of openISP
tjussh/focal-frequency-loss
[ICCV 2021] Focal Frequency Loss for Image Reconstruction and Synthesis
tjussh/ImageProcessing-Python
该资源为作者在CSDN的撰写Python图像处理文章的支撑，主要是Python实现图像处理、图像识别、图像分类等算法代码实现，希望该资源对您有所帮助，一起加油。
tjussh/imgaug
Image augmentation for machine learning experiments.
tjussh/InvDN
Implementation for the paper: Invertible Denoising Network: A Light Solution for Real Noise Removal (CVPR2021).
tjussh/Invertible-ISP
[CVPR2021] Invertible Image Signal Processing
tjussh/Jalali-Lab-Implementation-of-RAISR
Implementation of RAISR (Rapid and Accurate Image Super Resolution) algorithm in Python 3.x by Jalali Laboratory at UCLA. The implementation presented here achieved performance results that are comparable to that presented in Google's research paper (with less than ± 0.1 dB in PSNR). Just-in-time (JIT) compilation employing JIT numba is used to speed up the Python code. A very parallelized Python code employing multi-processing capabilities is used to speed up the testing process. The code has been tested on GNU/Linux and Mac OS X 10.13.2 platforms.
tjussh/KAIR
Image Restoration Toolbox (PyTorch). Training and testing codes for DPIR, USRNet, DnCNN, FFDNet, SRMD, DPSR, BSRGAN
tjussh/LDL
Official implementation of the paper 'Details or Artifacts: A Locally Discriminative Learning Approach to Realistic Image Super-Resolution' in CVPR 2022
tjussh/NBNet
Pytorch implement "NBNet: Noise Basis Learning for Image Denoising with Subspace Projection"
tjussh/openISP
Image Signal Processor
tjussh/Pretrained-IPT
tjussh/pytorch-loss-functions
A collection of loss functions with easy usage
tjussh/pytorch-receptive-field
Compute CNN receptive field size in pytorch in one line
tjussh/raisr
A Python implementation of RAISR
tjussh/sr_mobile_pytorch
A PyTorch port of `NJU-Jet/SR_Mobile_Quantization`
tjussh/SR_Mobile_Quantization
Winner solution of mobile AI (CVPRW 2021).
tjussh/traiNNer
traiNNer: Deep learning framework for image and video super-resolution, restoration and image-to-image translation, for training and testing.
tjussh/vit-pytorch
Implementation of Vision Transformer, a simple way to achieve SOTA in vision classification with only a single transformer encoder, in Pytorch
tjussh/XLSR
PyTorch implementation of paper "Extremely Lightweight Quantization Robust Real-Time Single-Image Super Resolution for Mobile Devices"