MVTec-Anomaly-DetectionThis project proposes an end-to-end framework for semi-supervised Anomaly Detection and Segmentation in images based on Deep Learning.
Stars: ✭ 161 (-86.69%)
PysadStreaming Anomaly Detection Framework in Python (Outlier Detection for Streaming Data)
Stars: ✭ 87 (-92.81%)
Isolation ForestA Spark/Scala implementation of the isolation forest unsupervised outlier detection algorithm.
Stars: ✭ 139 (-88.51%)
PyodA Python Toolbox for Scalable Outlier Detection (Anomaly Detection)
Stars: ✭ 5,083 (+320.08%)
CCDCode for 'Constrained Contrastive Distribution Learning for Unsupervised Anomaly Detection and Localisation in Medical Images' [MICCAI 2021]
Stars: ✭ 30 (-97.52%)
RemixautomlR package for automation of machine learning, forecasting, feature engineering, model evaluation, model interpretation, data generation, and recommenders.
Stars: ✭ 159 (-86.86%)
pytodTOD: GPU-accelerated Outlier Detection via Tensor Operations
Stars: ✭ 131 (-89.17%)
Novelty DetectionLatent space autoregression for novelty detection.
Stars: ✭ 152 (-87.44%)
dramaMain component extraction for outlier detection
Stars: ✭ 17 (-98.6%)
ros-yolo-sortYOLO v3, v4, v5, v6, v7 + SORT tracking + ROS platform. Supporting: YOLO with Darknet, OpenCV(DNN), OpenVINO, TensorRT(tkDNN). SORT supports python(original) and C++. (Not Deep SORT)
Stars: ✭ 162 (-86.61%)
deepOFTensorFlow implementation for "Guided Optical Flow Learning"
Stars: ✭ 26 (-97.85%)
msdaLibrary for multi-dimensional, multi-sensor, uni/multivariate time series data analysis, unsupervised feature selection, unsupervised deep anomaly detection, and prototype of explainable AI for anomaly detector
Stars: ✭ 80 (-93.39%)
sherlockSherlock is an anomaly detection service built on top of Druid
Stars: ✭ 137 (-88.68%)
mmselfsupOpenMMLab Self-Supervised Learning Toolbox and Benchmark
Stars: ✭ 2,315 (+91.32%)
salt iccv2017SALT (iccv2017) based Video Denoising Codes, Matlab implementation
Stars: ✭ 26 (-97.85%)
M-NMFAn implementation of "Community Preserving Network Embedding" (AAAI 2017)
Stars: ✭ 119 (-90.17%)
GuidedNetCaffe implementation for "Guided Optical Flow Learning"
Stars: ✭ 28 (-97.69%)
PANDAPANDA: Adapting Pretrained Features for Anomaly Detection and Segmentation (CVPR 2021)
Stars: ✭ 64 (-94.71%)
trafficA quick and dirty vehicle speed detector using video + anomaly detection
Stars: ✭ 21 (-98.26%)
ml galleryThis is a master project of some experiments with Neural Networks. Every project here is runnable, visualized and explained clearly.
Stars: ✭ 18 (-98.51%)
deepADDetection of Accounting Anomalies in the Latent Space using Adversarial Autoencoder Neural Networks - A lab we prepared for the KDD'19 Workshop on Anomaly Detection in Finance that will walk you through the detection of interpretable accounting anomalies using adversarial autoencoder neural networks. The majority of the lab content is based on J…
Stars: ✭ 65 (-94.63%)
LabelPropagationA NetworkX implementation of Label Propagation from a "Near Linear Time Algorithm to Detect Community Structures in Large-Scale Networks" (Physical Review E 2008).
Stars: ✭ 101 (-91.65%)
metric-transfer.pytorchDeep Metric Transfer for Label Propagation with Limited Annotated Data
Stars: ✭ 49 (-95.95%)
ViCC[WACV'22] Code repository for the paper "Self-supervised Video Representation Learning with Cross-Stream Prototypical Contrasting", https://arxiv.org/abs/2106.10137.
Stars: ✭ 33 (-97.27%)
USOT[ICCV2021] Learning to Track Objects from Unlabeled Videos
Stars: ✭ 52 (-95.7%)
amrOfficial adversarial mixup resynthesis repository
Stars: ✭ 31 (-97.44%)
unsup temp embedUnsupervised learning of action classes with continuous temporal embedding (CVPR'19)
Stars: ✭ 62 (-94.88%)
strollr2d icassp2017Image Denoising Codes using STROLLR learning, the Matlab implementation of the paper in ICASSP2017
Stars: ✭ 22 (-98.18%)
anomagramInteractive Visualization to Build, Train and Test an Autoencoder with Tensorflow.js
Stars: ✭ 152 (-87.44%)
BagelIPCCC 2018: Robust and Unsupervised KPI Anomaly Detection Based on Conditional Variational Autoencoder
Stars: ✭ 45 (-96.28%)
VAENAR-TTSPyTorch Implementation of VAENAR-TTS: Variational Auto-Encoder based Non-AutoRegressive Text-to-Speech Synthesis.
Stars: ✭ 66 (-94.55%)
ind knn adIndustrial knn-based anomaly detection for images. Visit streamlit link to check out the demo.
Stars: ✭ 102 (-91.57%)
FUSIONPyTorch code for NeurIPSW 2020 paper (4th Workshop on Meta-Learning) "Few-Shot Unsupervised Continual Learning through Meta-Examples"
Stars: ✭ 18 (-98.51%)
sutton-barto-rl-exercises📖Learning reinforcement learning by implementing the algorithms from reinforcement learning an introduction
Stars: ✭ 77 (-93.64%)
object-size-detector-pythonMonitor mechanical bolts as they move down a conveyor belt. When a bolt of an irregular size is detected, this solution emits an alert.
Stars: ✭ 26 (-97.85%)
DGFraud-TF2A Deep Graph-based Toolbox for Fraud Detection in TensorFlow 2.X
Stars: ✭ 84 (-93.06%)
IJCAI2018 SSDHSemantic Structure-based Unsupervised Deep Hashing IJCAI2018
Stars: ✭ 38 (-96.86%)
ailia-modelsThe collection of pre-trained, state-of-the-art AI models for ailia SDK
Stars: ✭ 1,102 (-8.93%)
VQ-APCVector Quantized Autoregressive Predictive Coding (VQ-APC)
Stars: ✭ 34 (-97.19%)
SESF-FuseSESF-Fuse: An Unsupervised Deep Model for Multi-Focus Image Fusion
Stars: ✭ 47 (-96.12%)
XGBODSupplementary material for IJCNN paper "XGBOD: Improving Supervised Outlier Detection with Unsupervised Representation Learning"
Stars: ✭ 59 (-95.12%)
deviation-network-imageOfficial PyTorch implementation of the paper “Explainable Deep Few-shot Anomaly Detection with Deviation Networks”, weakly/partially supervised anomaly detection, few-shot anomaly detection, image defect detection.
Stars: ✭ 47 (-96.12%)
FSSD OoD DetectionFeature Space Singularity for Out-of-Distribution Detection. (SafeAI 2021)
Stars: ✭ 66 (-94.55%)
lxa5Linguistica 5: Unsupervised Learning of Linguistic Structure
Stars: ✭ 27 (-97.77%)
Revisiting-Contrastive-SSLRevisiting Contrastive Methods for Unsupervised Learning of Visual Representations. [NeurIPS 2021]
Stars: ✭ 81 (-93.31%)
Deep-Unsupervised-Domain-AdaptationPytorch implementation of four neural network based domain adaptation techniques: DeepCORAL, DDC, CDAN and CDAN+E. Evaluated on benchmark dataset Office31.
Stars: ✭ 50 (-95.87%)
DETRegOfficial implementation of the CVPR 2022 paper "DETReg: Unsupervised Pretraining with Region Priors for Object Detection".
Stars: ✭ 273 (-77.44%)
volumetricPrimitivesCode release for "Learning Shape Abstractions by Assembling Volumetric Primitives " (CVPR 2017)
Stars: ✭ 137 (-88.68%)