MADCluster: Model-agnostic Anomaly Detection with Self-supervised Clustering Network
Fuente:
arXiv
Saved in:
| Main Authors: | Lee, Sangyong, Hwang, Subo, Kim, Dohoon |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Exploring the Task-agnostic Trait of Self-supervised Learning in the Context of Detecting Mental Disorders
by: Gupta, Rohan Kumar, et al.
Published: (2024)
by: Gupta, Rohan Kumar, et al.
Published: (2024)
Spatio-Temporal Graphs Beyond Grids: Benchmark for Maritime Anomaly Detection
by: Kim, Jeehong, et al.
Published: (2025)
by: Kim, Jeehong, et al.
Published: (2025)
Enhancing Anomaly Detection via Generating Diversified and Hard-to-distinguish Synthetic Anomalies
by: Kim, Hyuntae, et al.
Published: (2024)
by: Kim, Hyuntae, et al.
Published: (2024)
PRISP: Privacy-Safe Few-Shot Personalization via Lightweight Adaptation
by: Park, Junho, et al.
Published: (2026)
by: Park, Junho, et al.
Published: (2026)
Temporal Graph Networks for Graph Anomaly Detection in Financial Networks
by: Kim, Yejin, et al.
Published: (2024)
by: Kim, Yejin, et al.
Published: (2024)
Adaptive Self-supervised Robust Clustering for Unstructured Data with Unknown Cluster Number
by: Ding, Chen-Lu, et al.
Published: (2024)
by: Ding, Chen-Lu, et al.
Published: (2024)
Comparative Study on Supervised versus Semi-supervised Machine Learning for Anomaly Detection of In-vehicle CAN Network
by: Dong, Yongqi, et al.
Published: (2022)
by: Dong, Yongqi, et al.
Published: (2022)
ODIM: Outlier Detection via Likelihood of Under-Fitted Generative Models
by: Kim, Dongha, et al.
Published: (2023)
by: Kim, Dongha, et al.
Published: (2023)
Adaptive Sparsified Graph Learning Framework for Vessel Behavior Anomalies
by: Kim, Jeehong, et al.
Published: (2025)
by: Kim, Jeehong, et al.
Published: (2025)
Hybrid GCN-GRU Model for Anomaly Detection in Cryptocurrency Transactions
by: Na, Gyuyeon, et al.
Published: (2025)
by: Na, Gyuyeon, et al.
Published: (2025)
Unsupervised Anomaly Detection for Tabular Data Using Noise Evaluation
by: Dai, Wei, et al.
Published: (2024)
by: Dai, Wei, et al.
Published: (2024)
Label-based Graph Augmentation with Metapath for Graph Anomaly Detection
by: Kim, Hwan, et al.
Published: (2023)
by: Kim, Hwan, et al.
Published: (2023)
Fair Bayesian Model-Based Clustering
by: Lee, Jihu, et al.
Published: (2025)
by: Lee, Jihu, et al.
Published: (2025)
Towards a Unified Framework of Clustering-based Anomaly Detection
by: Fang, Zeyu, et al.
Published: (2024)
by: Fang, Zeyu, et al.
Published: (2024)
Computer Vision Self-supervised Learning Methods on Time Series
by: Lee, Daesoo, et al.
Published: (2021)
by: Lee, Daesoo, et al.
Published: (2021)
Self-supervised Graph Transformer with Contrastive Learning for Brain Connectivity Analysis towards Improving Autism Detection
by: Leng, Yicheng, et al.
Published: (2025)
by: Leng, Yicheng, et al.
Published: (2025)
Anomaly Detection with Adaptive and Aggressive Rejection for Contaminated Training Data
by: Lee, Jungi, et al.
Published: (2025)
by: Lee, Jungi, et al.
Published: (2025)
Causality-Aware Contrastive Learning for Robust Multivariate Time-Series Anomaly Detection
by: Kim, HyunGi, et al.
Published: (2025)
by: Kim, HyunGi, et al.
Published: (2025)
Leveraging Intermediate Representations of Time Series Foundation Models for Anomaly Detection
by: Han, Chan Sik, et al.
Published: (2025)
by: Han, Chan Sik, et al.
Published: (2025)
GDFlow: Anomaly Detection with NCDE-based Normalizing Flow for Advanced Driver Assistance System
by: Lee, Kangjun, et al.
Published: (2024)
by: Lee, Kangjun, et al.
Published: (2024)
On Diffusion Modeling for Anomaly Detection
by: Livernoche, Victor, et al.
Published: (2023)
by: Livernoche, Victor, et al.
Published: (2023)
Swift Hydra: Self-Reinforcing Generative Framework for Anomaly Detection with Multiple Mamba Models
by: Do, Nguyen, et al.
Published: (2025)
by: Do, Nguyen, et al.
Published: (2025)
Possibility for Proactive Anomaly Detection
by: Jeon, Jinsung, et al.
Published: (2025)
by: Jeon, Jinsung, et al.
Published: (2025)
Semi-supervised Anomaly Detection via Adaptive Reinforcement Learning-Enabled Method with Causal Inference for Sensor Signals
by: Chen, Xiangwei, et al.
Published: (2024)
by: Chen, Xiangwei, et al.
Published: (2024)
Model-agnostic Selective Labeling with Provable Statistical Guarantees
by: Huang, Huipeng, et al.
Published: (2025)
by: Huang, Huipeng, et al.
Published: (2025)
DoMIX: An Efficient Framework for Exploiting Domain Knowledge in Fine-Tuning
by: Kim, Dohoon, et al.
Published: (2025)
by: Kim, Dohoon, et al.
Published: (2025)
Trajectory Anomaly Detection with Language Models
by: Mbuya, Jonathan, et al.
Published: (2024)
by: Mbuya, Jonathan, et al.
Published: (2024)
Masked Diffusion Modeling for Anomaly Detection
by: Zhang, Lixing, et al.
Published: (2026)
by: Zhang, Lixing, et al.
Published: (2026)
Detect by Yourself: Self-Designing Agentic Workflows for Few-Shot Graph Anomaly Detection
by: Huang, Tairan, et al.
Published: (2026)
by: Huang, Tairan, et al.
Published: (2026)
Enabling Regional Explainability by Automatic and Model-agnostic Rule Extraction
by: Chen, Yu, et al.
Published: (2024)
by: Chen, Yu, et al.
Published: (2024)
Toward Reasoning on the Boundary: A Mixup-based Approach for Graph Anomaly Detection
by: Kim, Hwan, et al.
Published: (2024)
by: Kim, Hwan, et al.
Published: (2024)
Why the Counterintuitive Phenomenon of Likelihood Rarely Appears in Tabular Anomaly Detection with Deep Generative Models?
by: Kim, Donghwan, et al.
Published: (2026)
by: Kim, Donghwan, et al.
Published: (2026)
DEM: A Distilled Explanation Model for Interpretable Anomaly Detection in Physiological Sensor Networks
by: Singh, Jyotirmoy, et al.
Published: (2026)
by: Singh, Jyotirmoy, et al.
Published: (2026)
TAGCOS: Task-agnostic Gradient Clustered Coreset Selection for Instruction Tuning Data
by: Zhang, Jipeng, et al.
Published: (2024)
by: Zhang, Jipeng, et al.
Published: (2024)
AnomalyGFM: Graph Foundation Model for Zero/Few-shot Anomaly Detection
by: Qiao, Hezhe, et al.
Published: (2025)
by: Qiao, Hezhe, et al.
Published: (2025)
ReTabAD: A Benchmark for Restoring Semantic Context in Tabular Anomaly Detection
by: Yoon, Sanghyu, et al.
Published: (2025)
by: Yoon, Sanghyu, et al.
Published: (2025)
Self-supervised Pretraining for Decision Foundation Model: Formulation, Pipeline and Challenges
by: Liu, Xiaoqian, et al.
Published: (2023)
by: Liu, Xiaoqian, et al.
Published: (2023)
Fair Clustering via Alignment
by: Kim, Kunwoong, et al.
Published: (2025)
by: Kim, Kunwoong, et al.
Published: (2025)
When Model Meets New Normals: Test-time Adaptation for Unsupervised Time-series Anomaly Detection
by: Kim, Dongmin, et al.
Published: (2023)
by: Kim, Dongmin, et al.
Published: (2023)
Structure-based Anomaly Detection and Clustering
by: Leveni, Filippo
Published: (2025)
by: Leveni, Filippo
Published: (2025)
Similar Items
-
Exploring the Task-agnostic Trait of Self-supervised Learning in the Context of Detecting Mental Disorders
by: Gupta, Rohan Kumar, et al.
Published: (2024) -
Spatio-Temporal Graphs Beyond Grids: Benchmark for Maritime Anomaly Detection
by: Kim, Jeehong, et al.
Published: (2025) -
Enhancing Anomaly Detection via Generating Diversified and Hard-to-distinguish Synthetic Anomalies
by: Kim, Hyuntae, et al.
Published: (2024) -
PRISP: Privacy-Safe Few-Shot Personalization via Lightweight Adaptation
by: Park, Junho, et al.
Published: (2026) -
Temporal Graph Networks for Graph Anomaly Detection in Financial Networks
by: Kim, Yejin, et al.
Published: (2024)