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classic-convolution-network

里面会保存许多优秀的卷积神经网络结构，这些结构可以帮助我们更好的设计网络。

classical_ml_supervised

Collection of classical ML algorithms(Bayes, KNN, SVM) as well as dimensionality reduction techniques (PCA, LDA)

classification-of-cardiac-arrhythmia

Categorizing different categories of arrhythmia and for reducing the dimension six feature selection techniques (Random Forest, Variance Threshold, Analysis of Variance, CHI-2, dropping correlated features, and Recursive Feature Elimination) are applied in combination with fourteen classification algorithms (Support Vector Machine, Logistic Regression, Decision Tree, Random Forest,Gradient Boosting, Bagging, Naïve Bayes, K Nearest Neighbor, LightGBM, ID3, Stacking, Maximum Voting, and Averaging) using Python language.The aim is to compare different classification algorithms to predict cardiac arrhythmia diseases. The performance parameters taken are accuracy, precision, recall, and f-score.

classification_and_dimensionality-

Classifier and Dimensionality Reduction

clmlc

Clustering-based Local Multi-Label Classification

cluster

代码实现所有数据集的K-means，FCM，谱聚类，DBSCAN，AP（AffinityPropagation），DPC聚类算法比较

clustering

Comparison among K-Means, DBSCAN and Density Peak

clustering-1

Program to perform kmeans clustering, kernel kmeans, and also spectral clustering (last 2 both based on RBF kernels).

clustering-2

Clustering / Subspace Clustering Algorithms on MATLAB

clustering-of-facial-data-using-k-means

Alongwith the K-means algorithm, this project also involves principal component analysis for the dimensionality reduction and inturn speeding up the clustering of facial data

cnn

Using CNN features, SVM classifier and Transfer Learning

cnn-1

CNN AE Quantization/Denoising

cnn-for-image-retrieval

:sunrise:The code of post "Image retrieval using MatconvNet and pre-trained imageNet"

cnn_lstm_language_model

Implementation from scratch of a CNN-LSTM network for language models. Useful features are extracted from the CNN layer below and then feed up to the LSTM layer which forms a sequential context for the prediction.

coins

the COINS( CO-training for INductive Semi-supervised multi-label learning ) code package.

comparison-of-generative-rnn-based-models

Comparison of the RNN based Generative Models

computervision1

Implements image classification using handcrafted SIFT features as well as CNN extracted features for the course Computer Vision 1

convautoencoder_latentfeatures

A convolutional Autoencoder for similar object search based on Latent Space representation

cotnet

This is an official implementation for "Contextual Transformer Networks for Visual Recognition".

cross-gcn

Cross-GCN: Enhancing Graph Convolutional Network with k-Order Feature Interactions

cross-modal-retrieval

Activity image-based video retrieval

cs231b

CS 231B Cutting Edge of Computer Vision - Projects

cs519.github.io

cs549computervision

•Hybrid Image, Image Pyramid, Edge Detection •Detection, Description, and Matching •Face Recognition using Eigenface and Fisherface methods •Scene Recognition with Bag of Words •Final Project: Deep Convolutional Neural Network (CNN) and Support Vector Machine (SVM) in Pet Image Recognition

cv_template

一个图像复原或分割的统一框架，可以用于去雾🌫、去雨🌧、去模糊、夜景🌃复原、超分辨率👾、像素级分割等等。

cvpr14mtl

Scalable Multitask Representation Learning for Scene Classification

cvpr2022-papers-with-code-demo

收集 CVPR 最新的成果，包括论文、代码和demo视频等，欢迎大家推荐！

dae4muldoa

Fast DoA estimation of multiple targets using a Denoising Autoencoder and sparse arrays

dae_mnist_classification

DenoisingAutoEncoder For MNIST Classification.I use one-layer denoising autoencoder to extract the feature;Then use a softmax regression to classify the dataset.It can get an accuracy about 92%.

dasvm

Matlab implementation of the EM and MCMC algorithm for SVMs as introduced in the paper "Data augmentation for support vector machines"