https://gitlab.idiap.ch/bob/bob.paper.backdoors_anomaly_detection.biosig2022
공식 코드An anomaly detection approach for backdoored neural networks: face recognition as a case study
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://huggingface.co/datasets/agucci/mri-sym2
공식 코드Symbrain: A large-scale dataset of MRI images for neonatal brain symmetry analysis
https://bitbucket.org/paulpei/resgcn
ResGCN: Attention-based Deep Residual Modeling for Anomaly Detection on Attributed Networks
https://github.com/1010code/OneClass_NeuralNetwork
https://github.com/2023-MindSpore-1/ms-code-138
https://github.com/2023-MindSpore-1/ms-code-215/tree/main/PaDiM
PaDiM: a Patch Distribution Modeling Framework for Anomaly Detection and Localization
https://github.com/2023-MindSpore-1/ms-code-222/tree/main/CPNet
CPNet: Cross-Parallel Network for Efficient Anomaly Detection
https://github.com/2023-MindSpore-1/ms-code-223/tree/main/SSD
SSD: A Unified Framework for Self-Supervised Outlier Detection
https://github.com/2023-MindSpore-1/ms-code-26
Self-Supervised Predictive Convolutional Attentive Block for Anomaly Detection
https://github.com/2023-MindSpore-1/ms-code-4/tree/main/PatchCore
https://github.com/2023-MindSpore-4/Code4/tree/main/WS-VAD-mindspore-main
Weakly-Supervised Video Anomaly Detection with Snippet Anomalous Attention
https://github.com/2024-MindSpore-1/Code2/tree/main/model-1/auto
https://github.com/2024-MindSpore-1/Code6/tree/main/ADNet
https://github.com/2sang/OOD-baseline
A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks
https://github.com/5yearsKim/Conditional-Normalizing-Flow
https://github.com/A03ki/f-AnoGAN
Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discovery
https://github.com/AI-for-Ocean-Science/ulmo
https://github.com/Aaron-Pu/cma_xdVioDet
https://github.com/AbertayMachineLearningGroup/machine-learning-SIEM-water-infrastructure
Improving SIEM for Critical SCADA Water Infrastructures Using Machine Learning
https://github.com/AdamHtooLwin/vigilant
https://github.com/AhmedImtiazPrio/heartnet
https://github.com/AlexMeinke/Provable-OOD-Detection
https://github.com/AlexanderBogatko/Keras-CCVAE
https://github.com/AndreaBorghesi/anomaly_detection_HPC
Anomaly Detection using Autoencoders in High Performance Computing Systems
https://github.com/AndyYFTao/variational-autoencoder-mnist
https://github.com/AntixK/PyTorch-VAE
https://github.com/AnzorGozalishvili/autoencoders_playground
https://github.com/Ars235/Novelty_Detection
Adversarially Learned One-Class Classifier for Novelty Detection
https://github.com/Au3C2/GVS
https://github.com/Burf/tfdetection
Sub-Image Anomaly Detection with Deep Pyramid Correspondences
https://github.com/CIA-Oceanix/GeoTrackNet
https://github.com/Chandan-IITI/LMKAD
Localized Multiple Kernel Learning for Anomaly Detection: One-class Classification
https://github.com/ChengBinJin/VAE-Tensorflow
https://github.com/ClaraBing/flow
https://github.com/CompML/PRTS
https://github.com/CuberrChen/PaDiM-Paddle
PaDiM: a Patch Distribution Modeling Framework for Anomaly Detection and Localization
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.