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/kiransom/mnist_vae
https://github.com/kishordgupta/pyod
https://github.com/kittyvarghese/lightweight_saliency_detection
https://github.com/kjm1559/ADAE_LSTM_Autoencoder
https://github.com/kngwyu/pytorch-autoencoders
https://github.com/kobybibas/pnml_ood_detection
A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks
https://github.com/koheiobata/dmm
Fast and Multi-aspect Mining of Complex Time-stamped Event Streams
https://github.com/koheitokda/PaDiM
PaDiM: a Patch Distribution Modeling Framework for Anomaly Detection and Localization
https://github.com/kosyoshida/simple-keras
Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discovery
https://github.com/kota-dohi/dcase2022_task2_baseline_ae
https://github.com/kota-dohi/dcase2022_task2_baseline_mobile_net_v2
https://github.com/kpandey008/DiffuseVAE
https://github.com/kris-singh/ReadingList
https://github.com/kshmawj111/solar_energy_forecast
High-Dimensional Multivariate Forecasting with Low-Rank Gaussian Copula Processes
https://github.com/ktr-hubrt/WSAL
https://github.com/kuc2477/pytorch-vae
https://github.com/kyeongry/Autoencoders
https://github.com/kzkadc/alocc_mnist
Adversarially Learned One-Class Classifier for Novelty Detection
https://github.com/la3ma/IWAE-torch
https://github.com/lab-v2/PyEDCR
Rule-Based Error Detection and Correction to Operationalize Movement Trajectory Classification
https://github.com/lancopku/avg-avg
A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks
https://github.com/lanzhang128/disentanglement
https://github.com/larsmaaloee/BIVA
BIVA: A Very Deep Hierarchy of Latent Variables for Generative Modeling
https://github.com/lawyerphx/vae-practice
https://github.com/lee-seon-woo/tscp2_pytorch
Time Series Change Point Detection with Self-Supervised Contrastive Predictive Coding
https://github.com/leedoyup/anogan-tf
Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discovery
https://github.com/leezhi403/anomaly-detection-with-GAN-
Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discovery
https://github.com/leihuayi/NetworkEmbedding
https://github.com/lemonxiaohei/EfficientAD
EfficientAD: Accurate Visual Anomaly Detection at Millisecond-Level Latencies
https://github.com/leokster/CVAE
https://github.com/lflfdxfn/DPLAN-Implementation
Toward Deep Supervised Anomaly Detection: Reinforcement Learning from Partially Labeled Anomaly Data
https://github.com/liaoyuhua/GPT-TS
One Fits All:Power General Time Series Analysis by Pretrained LM
https://github.com/lifeitech/fce-2d
https://github.com/lifesailor/data-driven-predictive-maintenance
Unsupervised Detection of Anomalous Sound based on Deep Learning and the Neyman-Pearson Lemma
https://github.com/lilygeorgescu/AED-SSMTL
Anomaly Detection in Video via Self-Supervised and Multi-Task Learning
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.