DISYRE: Diffusion-Inspired Synthetic Restoration for Unsupervised Anomaly Detection
DISYRE: Diffusion-Inspired Synthetic Restoration for improved Unsupervised Anomaly Detection in medical images.
Integrating Dictionary Learning and One-Class Support Vector Machines for Unsupervised Anomaly Detection
The core message of this paper is to propose a new anomaly detection model that fuses dictionary learning and one-class support vector machines (OC-SVM) to improve unsupervised anomaly detection performance.
Multi-Image Visual Question Answering for Enhancing Interpretability of Unsupervised Anomaly Detection
Leveraging language models to provide detailed, understandable explanations for anomaly maps generated by unsupervised anomaly detection methods.
Multi-Feature Reconstruction Network with Crossed-Mask Restoration for Effective Unsupervised Anomaly Detection
A multi-feature reconstruction network using crossed-mask restoration is proposed to effectively detect anomalies in images without any labeled data.
Unsupervised Anomaly Detection via Masked Diffusion Posterior Sampling: A Novel and Highly-Interpretable Method for Robust Normal Image Reconstruction and Accurate Anomaly Localization
The proposed Masked Diffusion Posterior Sampling (MDPS) method models the problem of normal image reconstruction as multiple diffusion posterior samplings based on a devised masked noisy observation model and a diffusion-based normal image prior under Bayesian framework, enabling robust normal image reconstruction and accurate anomaly localization.
Adapted-MoE: A Mixture of Experts Model with Test-Time Adaptation for Robust Anomaly Detection in Diverse Datasets
Adapted-MoE addresses the challenges of feature distribution variations within the same category and distribution bias between training and test data by employing a Mixture of Experts model with a routing network and test-time adaptation.
Dual-Modeling Decouple Distillation (DMDD): A Novel Approach for Unsupervised Anomaly Detection in Images
This paper introduces DMDD, a novel knowledge distillation-based method for unsupervised anomaly detection in images, which leverages a decoupled student-teacher network architecture and dual-modeling distillation to achieve state-of-the-art localization performance by effectively capturing both the edges and centers of anomalies.