ZhaohuiXue
A Ph.D, graduated from Nanjing University, now working as a Professor in Hohai University, research interests including hyperspectral remote sensing, etc.
School of Earth Sciences and Engineering, Hohai University中国
ZhaohuiXue's Stars
LiLittleCat/awesome-free-chatgpt
🆓免费的 ChatGPT 镜像网站列表,持续更新。List of free ChatGPT mirror sites, continuously updated.
nndl/nndl.github.io
《神经网络与深度学习》 邱锡鹏著 Neural Network and Deep Learning
ctgk/PRML
PRML algorithms implemented in Python
rougier/scientific-visualization-book
An open access book on scientific visualization using python and matplotlib
spyder-ide/spyder
Official repository for Spyder - The Scientific Python Development Environment
dsgiitr/d2l-pytorch
This project reproduces the book Dive Into Deep Learning (https://d2l.ai/), adapting the code from MXNet into PyTorch.
fengdu78/WZU-machine-learning-course
温州大学《机器学习》课程资料(代码、课件等)
artix41/awesome-transfer-learning
Best transfer learning and domain adaptation resources (papers, tutorials, datasets, etc.)
CuriousAI/mean-teacher
A state-of-the-art semi-supervised method for image recognition
dsgiitr/graph_nets
PyTorch Implementation and Explanation of Graph Representation Learning papers: DeepWalk, GCN, GraphSAGE, ChebNet & GAT.
google-research/fixmatch
A simple method to perform semi-supervised learning with limited data.
clvrai/SSGAN-Tensorflow
A Tensorflow implementation of Semi-supervised Learning Generative Adversarial Networks (NIPS 2016: Improved Techniques for Training GANs).
eecn/Hyperspectral-Classification
Hyperspectral-Classification Pytorch
junjun-jiang/Hyperspectral-Image-Super-Resolution-Benchmark
A list of hyperspectral image super-solution resources collected by Junjun Jiang
iBelieveCJM/Tricks-of-Semi-supervisedDeepLeanring-Pytorch
PseudoLabel 2013, VAT, PI model, Tempens, MeanTeacher, ICT, MixMatch, FixMatch
nshaud/DeepHyperX
Deep learning toolbox based on PyTorch for hyperspectral data classification.
takerum/vat_tf
Virtual adversarial training with Tensorflow
Esri/raster-deep-learning
ArcGIS built-in python raster functions for deep learning to get you started fast.
s-laine/tempens
Temporal ensembling for semi-supervised learning
BehnoodRasti/HyFTech-Hyperspectral-Shallow-Deep-Feature-Extraction-Toolbox
This Toolbox includes Hyperspectral Feature Extraction Techniques including Unsupervised, Supervised, and Deep Feature Extraction
federhub/pyGRNN
Python implementation of General Regression Neural Network (Nadaraya-Watson Estimator). A Feature Selection module based on GRNN is also provided
caowenhan/hhuthesis
Aiming at the dissertations nonstandard format problems such as chart format, writing format and formula format, a simple and easy-to-use LaTeX template for Hohai dissertations is provided. The template strictly follows the requirements of the academic committee of Hohai University on the format of the dissertations and the corresponding national standards and specifications.
faruto/Libsvm-FarutoUltimate-Version
Libsvm-FarutoUltimate Version
peiyunh/mat-vae
A MATLAB implementation of Auto-Encoding Variational Bayes
ispamm/Lynx-Toolbox
Lynx Matlab Toolbox
IPL-UV/simpleR
A simple MATLAB regression toolbox
fuweijie/EAGR
Scalable Semi-Supervised Learning by Efficient Anchor Graph Regularization
fuweijie/AER
scalable active learning by approximated error reduction
fuweijie/HAGR
MohammedAhmedMagzoub/Face-recognition-using-collaborative-representation-and-LTV
Many algorithms for face recognition have been used in researches. Sparse representation based classification is an approach that classifies a sample with over complete dictionary. The testing can be recovered via L1 norm minimization. A newer Approach called Collaborative representation based classification uses the same way as Sparse representative, but it recovers the solution using L2 norm minimization. Both collaborative representation and sparse representation deal with only a small variation in pose and illumination. In this paper, we propose an approach to tackle the problem of illumination variation in collaborative representation. Our method is a combination between collaborative representation and logarithmic total variation (LTV). In this approach we are using LTV as a pre-processing step to our algorithm. LTV has made a huge impact on the result.