Pinned Repositories
ad_examples
A collection of anomaly detection methods (iid/point-based, graph and time series) including active learning for anomaly detection/discovery, bayesian rule-mining, description for diversity/explanation/interpretability. Analysis of incorporating label feedback with ensemble and tree-based detectors. Includes adversarial attacks with Graph Convolutional Network.
AI_Projects
Artificial Intelligence
ALOCC-CVPR2018
Adversarially Learned One-Class Classifier for Novelty Detection (ALOCC)
anomaly-detection-resources
Anomaly detection related books, papers, videos, and toolboxes
awesome-TS-anomaly-detection
List of tools & datasets for anomaly detection on time-series data.
Boruta-Shap
A Tree based feature selection tool which combines both the Boruta feature selection algorithm with shapley values.
boruta_py
Python implementations of the Boruta all-relevant feature selection method.
caffe_deep_learning_for_steganalysis
Cats_vs_Dogs
cleverhans
An adversarial example library for constructing attacks, building defenses, and benchmarking both
yoyo-cup's Repositories
yoyo-cup/Boruta-Shap
A Tree based feature selection tool which combines both the Boruta feature selection algorithm with shapley values.
yoyo-cup/Deep-Learning-on-Image-Denoising-An-overview
Deep Learning on Image Denoising: An overview (Neural Networks, 2020)
yoyo-cup/deeplabv3-plus-pytorch
这是一个deeplabv3-plus-pytorch的源码，可以用于训练自己的模型。
yoyo-cup/DudeNet
Designing and Training of A Dual CNN for Image Denoising (Knowledge-based Systems, 2021)
yoyo-cup/EGE-UNet
This is the official code repository for "EGE-UNet: an Efficient Group Enhanced UNet for skin lesion segmentation".
yoyo-cup/facies_classification_benchmark
The repository includes PyTorch code, and the data, to reproduce the results for our paper titled "A Machine Learning Benchmark for Facies Classification" (published in the SEG Interpretation Journal, August 2019).
yoyo-cup/faultSeg
Using synthetic datasets to train an end-to-end CNN for 3D fault segmentation (We are working on an improved version!)
yoyo-cup/FCN.tensorflow
Tensorflow implementation of Fully Convolutional Networks for Semantic Segmentation (http://fcn.berkeleyvision.org)
yoyo-cup/feature-engineering-tutorials
Data Science Feature Engineering and Selection Tutorials
yoyo-cup/feature-selection-for-machine-learning
Code repository for the online course Feature Selection for Machine Learning
yoyo-cup/FeatureSelectionGA
Feature Selection using Genetic Algorithm (DEAP Framework)
yoyo-cup/GLCM
Fast Gray-Level Co-Occurrence Matrix by numpy
yoyo-cup/hrnet-pytorch
这是一个hrnet-pytorch的库，可用于训练自己的语义分割数据集
yoyo-cup/HSENet
Hybrid-Scale Self-Similarity Exploitation for Remote Sensing Image Super-Resolution (accepted by TGRS)
yoyo-cup/Machine-Learning-Collection
A resource for learning about ML, DL, PyTorch and TensorFlow. Feedback always appreciated :)
yoyo-cup/MedUncertainty
Uncertainty in Medical Image Analysis
yoyo-cup/mmsegmentation
OpenMMLab Semantic Segmentation Toolbox and Benchmark.
yoyo-cup/pspnet-pytorch
这是一个pspnet-pytorch的源码，可以用于训练自己的模型。
yoyo-cup/pytorch-image-models
PyTorch image models, scripts, pretrained weights -- ResNet, ResNeXT, EfficientNet, NFNet, Vision Transformer (ViT), MobileNet-V3/V2, RegNet, DPN, CSPNet, Swin Transformer, MaxViT, CoAtNet, ConvNeXt, and more
yoyo-cup/Sampling-free-Epistemic-Uncertainty
Code for the ICCV 2019 paper "Sampling-free Epistemic Uncertainty Estimation Using Approximated Variance Propagation"
yoyo-cup/segmentation_models.pytorch
Segmentation models with pretrained backbones. PyTorch.
yoyo-cup/SegNet-Tutorial
Files for a tutorial to train SegNet for road scenes using the CamVid dataset
yoyo-cup/segyio
Fast Python library for SEGY files.
yoyo-cup/seismic_attributes
seismic attributes generator
yoyo-cup/seismic_deep_learning
A couple of python scripts to interpret geological structures from geophysical images using deep learning
yoyo-cup/SeismicSuperResolution
Repository for the paper "Deep Learning for Simultaneous Seismic Image Super-Resolution and Denoising" (IEEE TGRS)
yoyo-cup/ShapHT
Code: Adaptive feature selection with shapley and hypothetical testing: Case study of EEG feature engineering
yoyo-cup/Simple-SR
Include MuCAN, LAPAR, etc.
yoyo-cup/unet-pytorch
这是一个unet-pytorch的源码，可以训练自己的模型
yoyo-cup/vision
Datasets, Transforms and Models specific to Computer Vision