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optic-disc-and-cup-segmentation

Internship

optic-disc-detection-in-funds-image

Implemented an Algorithm to detect the center of Optic Disc using Image processing concepts and MATLAB as tool. Project was part of Bio-Medical Image Processing course.

optic-disc-unet

Attention Unet model with post process for retina optic disc segmention

optic-disk-cup-segmentation

Optic Disc and Optic Cup Segmentation using 57 layered deep convolutional neural network

optic-nerve-cnn

Code repository for a paper "Optic Disc and Cup Segmentation Methods for Glaucoma Detection with Modification of U-Net Convolutional Neural Network"

orca

Ordinal Regression and Classification Algorithms

p_net_anomaly_detection

This is the implementation of our paper in ECCV 2020.

palette

物体检测、图像分类在线API服务框架，从小宠物识别入手

pg_curve

Matlab code for computing and visualization: Confusion Matrix, Precision/Recall, ROC, Accuracy, F-Measure etc. for Classification.

pixpro

Propagate Yourself: Exploring Pixel-Level Consistency for Unsupervised Visual Representation Learning, CVPR 2021

ppml-tsa

Evaluating Privacy-Preserving Machine Learning in Critical Infrastructures: A Case Study on Time-Series Classification

prediction-based-video-anomaly-detection-

pseudo2go

A graph-based deep learning for predicting pseudogene functions by borrowing information from coding genes

pseudo_label-pytorch

The Simple and Efficient Semi-Supervised Learning Method for Deep Neural Networks

pyemd

经验模态分解 (Empirical Mode Decomposition)

pyod

(JMLR'19) A Python Toolbox for Scalable Outlier Detection (Anomaly Detection)

python-machine-learning-algorithm

Python-Machine-Learning-Algorithm

pytorch-tutorial

Build your neural network easy and fast

pytorch-tutorial-1

PyTorch Tutorial for Deep Learning Researchers

r-gae

rasnmf

Asymmetric Semi-Nonnegative Matrix Factorization for Directed Graph Clustering

rca

Implementation for paper: RCA: A Deep Collaborative Autoencoder Approach for Anomaly Detection

rcl-code

Implementation of Robust Collaborative Learning

reconstruction-by-inpainting-for-visual-anomaly-detection

This is an unofficial implementation of Reconstruction by inpainting for visual anomaly detection (RIAD).

recurrent-attention-model

The Recurrent Attention Model is a deep model that predicts the label of an image that is cluttered with bits from other images (noisy image) and predict the original image using the label. It has drawn inspiration from the Google's Visual Attention Model in 2014. The scope of the project is expanded to use this model on colored images and use the Inception Net model for feature extraction.

red-cnn

Pytorch Implementation of Low-Dose CT with a Residual Encoder-Decoder Convolutional Neural Network (RED-CNN)

regnet

Regularizing deep networks (for classification)

reproduce-l-s-dynamic-mri

Matlab code for reproducing 2019 IEEE Tr. Comp. Im. paper "Efficient Dynamic Parallel MRI Reconstruction for the Low-Rank Plus Sparse Model" by Claire Lin

reproducemvtec

Reproduce the CVPR2020 paper: Uninformed Students: Student-Teacher Anomaly Detection With Discriminative Latent Embeddings

research_paper-lstm-stacked-ae-to-unsupervised-multivariate-forecasting