Image de couverture de livre

Training-Free Time-Series Anomaly Detection: Leveraging Image Foundation Models

Fuente: arXiv

Enregistré dans:

Détails bibliographiques
Auteurs principaux:	Namura, Nobuo, Ichikawa, Yuma
Format:	Preprint
Publié:	2024
Sujets:	Machine Learning
Accès en ligne:	Acceder al recurso
Tags:	Ajouter un tag Pas de tags, Soyez le premier à ajouter un tag!

Documents similaires

Regional Expected Improvement for Efficient Trust Region Selection in High-Dimensional Bayesian Optimization
par: Namura, Nobuo, et autres
Publié: (2024)

Best-Arm Identification-Based Trust Region Selection for Bayesian Optimization on Multimodal Functions
par: Namura, Nobuo, et autres
Publié: (2026)

Leveraging Intermediate Representations of Time Series Foundation Models for Anomaly Detection
par: Han, Chan Sik, et autres
Publié: (2025)

ChronosAD: Leveraging Time Series Foundation Models for Accurate Anomaly Detection
par: Khan, Uzair, et autres
Publié: (2026)

Quantization Error Propagation: Revisiting Layer-Wise Post-Training Quantization
par: Arai, Yamato, et autres
Publié: (2025)

Controlling Continuous Relaxation for Combinatorial Optimization
par: Ichikawa, Yuma
Publié: (2023)

Leveraging Time-Series Foundation Model for Subsurface Well Logs Prediction and Anomaly Detection
par: Koeshidayatullah, Ardiansyah, et autres
Publié: (2024)

Time Series Foundational Models: Their Role in Anomaly Detection and Prediction
par: Shyalika, Chathurangi, et autres
Publié: (2024)

TimeRadar: A Domain-Rotatable Foundation Model for Time Series Anomaly Detection
par: He, Hui, et autres
Publié: (2026)

Label-Free Multivariate Time Series Anomaly Detection
par: Zhou, Qihang, et autres
Publié: (2023)

Towards Foundation Models for Zero-Shot Time Series Anomaly Detection: Leveraging Synthetic Data and Relative Context Discrepancy
par: Lan, Tian, et autres
Publié: (2025)

RATFM: Retrieval-augmented Time Series Foundation Model for Anomaly Detection
par: Maru, Chihiro, et autres
Publié: (2025)

High-dimensional Asymptotics of VAEs: Threshold of Posterior Collapse and Dataset-Size Dependence of Rate-Distortion Curve
par: Ichikawa, Yuma, et autres
Publié: (2023)

THEMIS: Unlocking Pretrained Knowledge with Foundation Model Embeddings for Anomaly Detection in Time Series
par: Lorik, Yadav Mahesh, et autres
Publié: (2025)

A Comparative Study of Adaptation Strategies for Time Series Foundation Models in Anomaly Detection
par: Park, Miseon, et autres
Publié: (2026)

Towards Foundation Auto-Encoders for Time-Series Anomaly Detection
par: González, Gastón García, et autres
Publié: (2025)

Adaptive Conformal Anomaly Detection with Time Series Foundation Models for Signal Monitoring
par: Gil, Natalia Martinez, et autres
Publié: (2026)

STAR: Boosting Time Series Foundation Models for Anomaly Detection through State-aware Adapter
par: Cheng, Hanyin, et autres
Publié: (2025)

Optimization by Parallel Quasi-Quantum Annealing with Gradient-Based Sampling
par: Ichikawa, Yuma, et autres
Publié: (2024)

Continuous Parallel Relaxation for Finding Diverse Solutions in Combinatorial Optimization Problems
par: Ichikawa, Yuma, et autres
Publié: (2024)

TimeSeriesBench: An Industrial-Grade Benchmark for Time Series Anomaly Detection Models
par: Si, Haotian, et autres
Publié: (2024)

Leveraging Generic Time Series Foundation Models for EEG Classification
par: Gnassounou, Théo, et autres
Publié: (2025)

Sign Lock-In: Randomly Initialized Weight Signs Persist and Bottleneck Sub-Bit Model Compression
par: Sakai, Akira, et autres
Publié: (2026)

Selective Denoising Diffusion Model for Time Series Anomaly Detection
par: Obata, Kohei, et autres
Publié: (2026)

Single and Multi-Objective Optimization Benchmark Problems Focusing on Human-Powered Aircraft Design
par: Namura, Nobuo
Publié: (2023)

Statistical Mechanics of Min-Max Problems
par: Ichikawa, Yuma, et autres
Publié: (2024)

AXIS: Explainable Time Series Anomaly Detection with Large Language Models
par: Lan, Tian, et autres
Publié: (2025)

Contextual and Seasonal LSTMs for Time Series Anomaly Detection
par: Zhang, Lingpei, et autres
Publié: (2026)

Graph Anomaly Detection in Time Series: A Survey
par: Ho, Thi Kieu Khanh, et autres
Publié: (2023)

Open-Set Multivariate Time-Series Anomaly Detection
par: Lai, Thomas, et autres
Publié: (2023)

Forecast2Anomaly (F2A): Adapting Multivariate Time Series Foundation Models for Anomaly Prediction
par: Hassan, Atif, et autres
Publié: (2025)

Explainable Time Series Anomaly Detection using Masked Latent Generative Modeling
par: Lee, Daesoo, et autres
Publié: (2023)

Physics-Informed Diffusion Models for Unsupervised Anomaly Detection in Multivariate Time Series
par: Soni, Juhi, et autres
Publié: (2025)

Training-Free Zero-Shot Anomaly Detection in 3D Brain MRI with 2D Foundation Models
par: Le-Gia, Tai, et autres
Publié: (2026)

How Foundational are Foundation Models for Time Series Forecasting?
par: Karaouli, Nouha, et autres
Publié: (2025)

MIXAD: Memory-Induced Explainable Time Series Anomaly Detection
par: Kim, Minha, et autres
Publié: (2024)

Weakly Augmented Variational Autoencoder in Time Series Anomaly Detection
par: Wu, Zhangkai, et autres
Publié: (2024)

Multivariate Time Series Anomaly Detection in Industry 5.0
par: Colombi, Lorenzo, et autres
Publié: (2025)

Formally Exploring Time-Series Anomaly Detection Evaluation Metrics
par: Wagner, Dennis, et autres
Publié: (2025)

Open Challenges in Time Series Anomaly Detection: An Industry Perspective
par: Mueller, Andreas
Publié: (2025)