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/google/flax/tree/master/examples/vae
https://github.com/grand-lab/cola
Anomaly Detection on Attributed Networks via Contrastive Self-Supervised Learning
https://github.com/greenelab/tybalt
https://github.com/greentfrapp/keras-aae
https://github.com/guillem96/madgan-pytorch
MAD-GAN: Multivariate Anomaly Detection for Time Series Data with Generative Adversarial Networks
https://github.com/guome/EfficientAD
EfficientAD: Accurate Visual Anomaly Detection at Millisecond-Level Latencies
https://github.com/guyAmit/GLOD
A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks
https://github.com/h19920918/memae
https://github.com/halixness/generative_timesformer_pytorch
Is Space-Time Attention All You Need for Video Understanding?
https://github.com/hamrel-cxu/EnbPI
Conformal Anomaly Detection on Spatio-Temporal Observations with Missing Data
https://github.com/haochenglouis/robusttsf
https://github.com/harish678/PyTorch-Lightning-Examples/blob/master/vae_lightning.py
https://github.com/hcw-00/PatchCore_anomaly_detection
https://github.com/hcw-00/STPM_anomaly_detection
Student-Teacher Feature Pyramid Matching for Anomaly Detection
https://github.com/henry1985/EfficientAD
EfficientAD: Accurate Visual Anomaly Detection at Millisecond-Level Latencies
https://github.com/highwaywu/imageforensicsosn
https://github.com/hikmatkhan/DeepGen
https://github.com/hitsz-tmg/videovista
VideoVista: A Versatile Benchmark for Video Understanding and Reasoning
https://github.com/hmyao22/DADF
Visual Anomaly Detection via Dual-Attention Transformer and Discriminative Flow
https://github.com/honeywell21/DLinear
https://github.com/hrbigelow/draw
https://github.com/hse-cs/probaforms
https://github.com/hsinyilin19/resnetvae
https://github.com/hungdhv97/Glow-Generative-Flow-with-Invertible-1x1-Convolutions
https://github.com/hussein-ha1601589/N-BaIoT-reloaded
N-BaIoT: Network-based Detection of IoT Botnet Attacks Using Deep Autoencoders
https://github.com/hwalsuklee/tensorflow-mnist-VAE
https://github.com/iitis/CumulantsFeatures.jl
https://github.com/ikostrikov/pytorch-flows
https://github.com/ilialexander/htmau
https://github.com/ilialexander/rsm
https://github.com/ingbeeedd/PaDiM-EfficientNet
PaDiM: a Patch Distribution Modeling Framework for Anomaly Detection and Localization
https://github.com/ioannislivieris/dlinear
https://github.com/iskandergaba/pyriodicity
RobustPeriod: Time-Frequency Mining for Robust Multiple Periodicity Detection
https://github.com/jackson-kang/pytorch-vae-tutorial
https://github.com/jafarbakhshaliyev/wave-augs
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.