https://github.com/spencerbraun/anomaly_transformer_pytorch
Anomaly Transformer: Time Series Anomaly Detection with Association Discrepancy
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/srinidhiPY/SSL_CR_Histo
https://github.com/sterngerlach/deep-learning-chapter-19
https://github.com/stevenboys/moon
OpenOOD: Benchmarking Generalized Out-of-Distribution Detection
https://github.com/sushovanjena/Attention-aware-Entropy-Distillation-for-Anomaly-Detection
Attend, Distill, Detect: Attention-aware Entropy Distillation for Anomaly Detection
https://github.com/syyunn/node2vec
https://github.com/t22m003/EfficientAD
EfficientAD: Accurate Visual Anomaly Detection at Millisecond-Level Latencies
https://github.com/tSchlegl/f-AnoGAN
Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discovery
https://github.com/taikiinoue45/PaDiM
PaDiM: a Patch Distribution Modeling Framework for Anomaly Detection and Localization
https://github.com/taikiinoue45/PatchCore
https://github.com/taikiinoue45/RIAD
https://github.com/tanakai0/SensorDataSimulator
Sensor Data Simulation for Anomaly Detection of the Elderly Living Alone
https://github.com/taohan10200/weather-5k
https://github.com/taohu88/BayesianML
https://github.com/tbcvContributor/DeepHawkeye
https://github.com/tegg89/VAE-Tensorflow
https://github.com/temirgaliyev/mnist_vae
https://github.com/teo-sl/DPLAN_pytorch
Toward Deep Supervised Anomaly Detection: Reinforcement Learning from Partially Labeled Anomaly Data
https://github.com/thetak11/pca_shapley
A Characteristic Function for Shapley-Value-Based Attribution of Anomaly Scores
https://github.com/thuiar/textoir
A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks
https://github.com/thuiar/textoir-demo
A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks
https://github.com/thuml/Time-Series-Library
TimesNet: Temporal 2D-Variation Modeling for General Time Series Analysis
https://github.com/thuml/timesnet
TimesNet: Temporal 2D-Variation Modeling for General Time Series Analysis
https://github.com/tianwangbuaa/VAE-for-abnormal-event-detection
https://github.com/tientrandinh/revisiting-reverse-distillation
https://github.com/timbmg/VAE-CVAE-MNIST
https://github.com/timsainb/Tensorflow-MultiGPU-VAE-GAN
https://github.com/timyadnyda/variational-lstm-autoencoder
A Multimodal Anomaly Detector for Robot-Assisted Feeding Using an LSTM-based Variational Autoencoder
https://github.com/tiskw/patchcore-ad
https://github.com/tkdleksms/LG_ES_anomaly
https://github.com/tkwoo/anogan-keras
Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discovery
https://github.com/tmiranda101/OC-NN_remote
https://github.com/tnybny/Frame-level-anomalies-in-videos
https://github.com/tonystevenj/vae-celeba-pytorch-lightning
https://github.com/totoroKalic/patchcore-mindspore
https://github.com/towhee-io/towhee
Is Space-Time Attention All You Need for Video Understanding?
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.