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Supervised Anomaly Detection

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In the training set, the amount of abnormal samples is limited and significant fewer than normal samples, producing data distributions that lead to a naturally imbalanced learning problem.

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/Armanfard-Lab/DASVDD

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DASVDD: Deep Autoencoding Support Vector Data Descriptor for Anomaly Detection

https://github.com/Choubo/deviation-network-image

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Explainable Deep Few-shot Anomaly Detection with Deviation Networks

https://github.com/Dongzhijin/MNPWAD

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Multi-Normal Prototypes Learning for Weakly Supervised Anomaly Detection

https://github.com/Fraunhofer-AISEC/A3

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$\text{A}^3$: Activation Anomaly Analysis

https://github.com/Fraunhofer-AISEC/R2-AD2

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R2-AD2: Detecting Anomalies by Analysing the Raw Gradient

https://github.com/Juintin/GraphAnomalyDetection

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Hop-Count Based Self-Supervised Anomaly Detection on Attributed Networks

https://github.com/MahsaMesgaran/GFCN

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Graph Fairing Convolutional Networks for Anomaly Detection

https://github.com/abc-125/segad

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Supervised Anomaly Detection for Complex Industrial Images

https://github.com/ahxmeds/igconda-pet

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IgCONDA-PET: Weakly-Supervised PET Anomaly Detection using Implicitly-Guided Attention-Conditional Counterfactual Diffusion Modeling -- a Multi-Center, Multi-Cancer, and Multi-Tracer Study

https://github.com/aimagelab/Consistency-based-Self-supervised-Learning-for-Temporal-Anomaly-Localization

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Consistency-based Self-supervised Learning for Temporal Anomaly Localization

https://github.com/antoine-spahr/SELF-TAUGHT-SEMI-SUPERVISED-ANOMALY-DETECTION

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Self-Taught Semi-Supervised Anomaly Detection on Upper Limb X-rays

https://github.com/aoudsung/tri-crlad

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Semi-supervised Anomaly Detection via Adaptive Reinforcement Learning-Enabled Method with Causal Inference for Sensor Signals

https://github.com/cbmi-group/adfa

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ADFA: Attention-augmented Differentiable top-k Feature Adaptation for Unsupervised Medical Anomaly Detection

https://github.com/choubo/dra

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Catching Both Gray and Black Swans: Open-set Supervised Anomaly Detection

https://github.com/cool-xuan/anoonly

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AnoOnly: Semi-Supervised Anomaly Detection with the Only Loss on Anomalies

https://github.com/cool-xuan/imbalanced_sam

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ImbSAM: A Closer Look at Sharpness-Aware Minimization in Class-Imbalanced Recognition

https://github.com/d10andy/sad

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SAD: Semi-Supervised Anomaly Detection on Dynamic Graphs

https://github.com/donghao51/nng-mix

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NNG-Mix: Improving Semi-supervised Anomaly Detection with Pseudo-anomaly Generation

https://github.com/erktkdg/MTFL

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MTFL: Multi-Timescale Feature Learning for Weakly-Supervised Anomaly Detection in Surveillance Videos

https://github.com/esa/gaia-astrometric-exoplanet-orbit-ml

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Machine learning-based identification of Gaia astrometric exoplanet orbits

https://github.com/flyinghu123/cpr

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Target before Shooting: Accurate Anomaly Detection and Localization under One Millisecond via Cascade Patch Retrieval

https://github.com/gaobb/anogen

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Few-Shot Anomaly-Driven Generation for Anomaly Classification and Segmentation

https://github.com/hep-lbdl/gaiacwola

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Weakly-Supervised Anomaly Detection in the Milky Way

https://github.com/hmsch/natural-synthetic-anomalies

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Natural Synthetic Anomalies for Self-Supervised Anomaly Detection and Localization

https://github.com/iamcuriosity/wscxr

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Weakly Supervised Anomaly Detection for Chest X-Ray Image

https://github.com/igcondapet/igconda-pet

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IgCONDA-PET: Weakly-Supervised PET Anomaly Detection using Implicitly-Guided Attention-Conditional Counterfactual Diffusion Modeling -- a Multi-Center, Multi-Cancer, and Multi-Tracer Study

https://github.com/ividja/Probabilistic-PII

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Confidence-Aware and Self-Supervised Image Anomaly Localisation

https://github.com/junha-kim/Learning-to-Adapt-to-Unseen-Abnormal-Activities

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Learning to Adapt to Unseen Abnormal Activities under Weak Supervision

https://github.com/jx-zhong-for-academic-purpose/GCN-Anomaly-Detection

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Graph Convolutional Label Noise Cleaner: Train a Plug-and-play Action Classifier for Anomaly Detection

https://github.com/jzc777/mapl

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MAPL: Memory Augmentation and Pseudo-Labeling for Semi-Supervised Anomaly Detection

https://github.com/lcs-crr/path

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PATH: A Discrete-sequence Dataset for Evaluating Online Unsupervised Anomaly Detection Approaches for Multivariate Time Series

https://github.com/lukasruff/Deep-SAD-PyTorch

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Deep Semi-Supervised Anomaly Detection

https://github.com/m-code-space/sadde

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SADDE: Semi-supervised Anomaly Detection with Dependable Explanations

https://github.com/mala-lab/ahl

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Anomaly Heterogeneity Learning for Open-set Supervised Anomaly Detection

https://github.com/mala-lab/prenet

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Deep Weakly-supervised Anomaly Detection

https://github.com/mattlaued/xor-is-linearly-classifiable

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Revisiting Non-separable Binary Classification and its Applications in Anomaly Detection

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