Papers with Code 아카이브
Anomaly Detection
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Anomaly Detection is a binary classification identifying unusual or unexpected patterns in a dataset, which deviate significantly from the majority of the data. The goal of anomaly detection is to identify such anomalies, which could represent errors, fraud, or other types of unusual events, and flag them for further investigation.
[Image source]: GAN-based Anomaly Detection in Imbalance Problems
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/wufan-tb/gmm_dae
Video Anomaly Detection by Estimating Likelihood of Representations
https://github.com/wxdang/MSCRED
https://github.com/wxie9/card
https://github.com/x0v/A-Weighted-Mutual-k-Nearest-Neighbour-for-Classification-Mining
A Weighted Mutual k-Nearest Neighbour for Classification Mining
https://github.com/xandery-geek/BackdoorAttacks
FIBA: Frequency-Injection based Backdoor Attack in Medical Image Analysis
https://github.com/xiahaifeng1995/FAVAE-anomaly-detection-localization-master
https://github.com/xiahaifeng1995/PaDiM-Anomaly-Detection-Localization-master
PaDiM: a Patch Distribution Modeling Framework for Anomaly Detection and Localization
https://github.com/xiahaifeng1995/STPM-Anomaly-Detection-Localization-master
Student-Teacher Feature Pyramid Matching for Anomaly Detection
https://github.com/xiangyu-liu/Computer-Network
https://github.com/xingyu617/SimTS_Representation_Learning
https://github.com/xnought/vae-explainer
https://github.com/xtarx/Unsupervised-Anomaly-Detection-with-Generative-Adversarial-Networks
Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discovery
https://github.com/xu737/PeFAD
Detecting Spacecraft Anomalies Using LSTMs and Nonparametric Dynamic Thresholding
https://github.com/y-bar/ml-based-anomaly-detection
https://github.com/y-bar/spectral-residual
https://github.com/y-kawagu/dcase2021_task2_baseline_ae
https://github.com/y-kawagu/dcase2021_task2_baseline_mobile_net_v2
https://github.com/y-kawagu/dcase2021_task2_evaluator
https://github.com/y0ast/Glow-PyTorch
https://github.com/y0ast/VAE-TensorFlow
https://github.com/y0ast/Variational-Autoencoder
https://github.com/yangyucheng000/patchcore
https://github.com/yaroslav-moiseev/evidence-based-possibly-best-practices-in-classical-ML
https://github.com/ydmunck/patch_SVDD
Patch SVDD: Patch-level SVDD for Anomaly Detection and Segmentation
https://github.com/yhy258/VariationalAutoEncoders-Pytorch
https://github.com/yiyixuxu/TimeSformer-rolled-attention
Is Space-Time Attention All You Need for Video Understanding?
https://github.com/yj-zhou/Feature_Encoding_with_AutoEncoders_for_Weakly-supervised_Anomaly_Detection
Feature Encoding with AutoEncoders for Weakly-supervised Anomaly Detection
https://github.com/yjlolo/vae-audio
https://github.com/yjucho1/articles
https://github.com/yoshinaga0106/spectral-residual
https://github.com/yuetan031/taddy_pytorch
https://github.com/yunchispk/WPS_PAKDD
Unsupervised Anomaly Detection via Variational Auto-Encoder for Seasonal KPIs in Web Applications
https://github.com/yunseokddi/DeepSAD_pytorch
https://github.com/yyliu01/it2
Residual Pattern Learning for Pixel-wise Out-of-Distribution Detection in Semantic Segmentation
https://github.com/yzhao062/yzhao062
ADMoE: Anomaly Detection with Mixture-of-Experts from Noisy Labels
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.