Zhaohui Xue's starred repositories
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.
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.
WZU-machine-learning-course
温州大学《机器学习》课程资料（代码、课件等）
scientific-visualization-book
An open access book on scientific visualization using python and matplotlib
HyFTech-Hyperspectral-Shallow-Deep-Feature-Extraction-Toolbox
This Toolbox includes Hyperspectral Feature Extraction Techniques including Unsupervised, Supervised, and Deep Feature Extraction
nndl.github.io
《神经网络与深度学习》 邱锡鹏著 Neural Network and Deep Learning
Hyperspectral-Classification
Hyperspectral-Classification Pytorch
DeepHyperX
Deep learning toolbox based on PyTorch for hyperspectral data classification.
Tricks-of-Semi-supervisedDeepLeanring-Pytorch
PseudoLabel 2013, VAT, PI model, Tempens, MeanTeacher, ICT, MixMatch, FixMatch
raster-deep-learning
ArcGIS built-in python raster functions for deep learning to get you started fast.
Lynx-Toolbox
Lynx Matlab Toolbox
graph_nets
PyTorch Implementation and Explanation of Graph Representation Learning papers: DeepWalk, GCN, GraphSAGE, ChebNet & GAT.
d2l-pytorch
This project reproduces the book Dive Into Deep Learning (https://d2l.ai/), adapting the code from MXNet into PyTorch.
Libsvm-FarutoUltimate-Version
Libsvm-FarutoUltimate Version
Hyperspectral-Image-Super-Resolution-Benchmark
A list of hyperspectral image super-solution resources collected by Junjun Jiang
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.
awesome-transfer-learning
Best transfer learning and domain adaptation resources (papers, tutorials, datasets, etc.)
mean-teacher
A state-of-the-art semi-supervised method for image recognition
SSGAN-Tensorflow
A Tensorflow implementation of Semi-supervised Learning Generative Adversarial Networks (NIPS 2016: Improved Techniques for Training GANs).