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awesome-graph-classification

A collection of important graph embedding, classification and representation learning papers with implementations.

bagging-boosting-random-forests

Bagging, boosting and random forests in Matlab

bicyclegan

[NIPS 2017] Toward Multimodal Image-to-Image Translation

cheng

deeplearning-500-questions

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18个章节，50余万字。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

gaussian-mixture-models

implementation of expectation maximization algorithm for gaussian mixture models and comparing it with non-parametric histogram estimation

gg-rician-sar-image-modelling

A Rician distribution based novel statistical model for modelling the intensity and amplitude SAR images

gnn_review

GNN综述阅读报告

grabcut

This a project to implement the algorithm of GrabCut

graphcut-cnn

hyperspectral-image-classification

Hyperspectral Image (HSI) classification with matlab

ieee_tgrs_gcn

Danfeng Hong, Lianru Gao, Jing Yao, Bing Zhang, Antonio Plaza, Jocelyn Chanussot. Graph Convolutional Networks for Hyperspectral Image Classification, IEEE TGRS, 2020.

igarss2020_bwms

Codes for IGARSS2020 paper: Band-Wise Multi-Scale CNN Architecture for Remote Sensing Image Scene Classification.

matconvnet-18

MatConvNet: CNNs for MATLAB

matresnet

Residual networks with MatConvNet

mtcnn_face_detection_alignment

Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Neural Networks

multi-scale-cnn-based-mrf-for-image-segmentation

The code is for image segmentation. The MSCNN.py is implemented using keras with tensorflow as backend. The *.m files are implemented on MATLAB.

non-linear-classifier

Classification of handwritten MNIST data using SVM, RVM, Gaussian process model

patternrecognition_matlab

Feature reduction projections and classifier models are learned by training dataset and applied to classify testing dataset. A few approaches of feature reduction have been compared in this paper: principle component analysis (PCA), linear discriminant analysis (LDA) and their kernel methods (KPCA,KLDA). Correspondingly, a few approaches of classification algorithm are implemented: Support Vector Machine (SVM), Gaussian Quadratic Maximum Likelihood and K-nearest neighbors (KNN) and Gaussian Mixture Model(GMM).

poldecomp

Polarimetric Synthetic Aperture Radar (PolSAR) Decomposition Modelling

sar-synthetic-aperture-radar

合成孔径雷达 相关。研究生期间学习 SAR/InSAR/PolSAR 相关的代码和总结，毕业后已经离开这个领域了。分享出来，仅此纪念。1）SAR: 成像算法，RD，CS，Radarsat-1数据成像处理。2）InSAR: 人造场景原始回波仿真、成像及干涉处理。包括平地场景和圆锥形场景。3）PolSAR: 极化定标算法，Whitt, PARC, Quegan, Ainsworth。详见 readme.md

semi_supervised_learning_tutorial

Tutorial presentation and code showing a semi-supervised Gaussian Mixture Model

seven-component-scattering-power-decomposition-of-polsar-coherency-matrix

Singh's Seven-Component Scattering Power Decomposition

sgmm

Supervised Gaussian Mixture Models

source_code

I share the source codes of my previously published papers.

ssan

Spectral-Spatial Attention Network for Hyperspectral Image Classification

sumproduct

Sum product algorithm - Belief propagation (message passing) for factor graphs