Unsupervised Anomaly Detection in Ultrasound Imaging Using Synthetic Anomaly Noise and Multi-Stage Diffusion
This paper introduces a novel unsupervised anomaly detection framework for medical images, particularly effective for ultrasound imaging, using a diffusion model trained with synthetic anomaly noise and a multi-stage diffusion process for enhanced accuracy and detail preservation.
Dinomaly: Achieving State-of-the-Art Multi-Class Unsupervised Anomaly Detection with a Simplified Transformer Approach
Dinomaly, a novel framework for multi-class unsupervised anomaly detection, achieves state-of-the-art performance by leveraging a simplified Transformer architecture with four key elements: foundation Transformers, a noisy bottleneck, linear attention, and loose reconstruction.
A Survey on Unsupervised Industrial Anomaly Detection Using RGB, 3D, and Multimodal Approaches
This survey paper provides a comprehensive overview of unsupervised anomaly detection methods in industrial settings, focusing on RGB, 3D, and multimodal approaches. It categorizes existing methods, discusses their strengths and weaknesses, and highlights future research directions.
How Selecting a Few Prototypical In-Distribution Samples Can Improve Unsupervised Anomaly Detection
Training unsupervised anomaly detection models on a carefully selected subset of prototypical in-distribution samples can outperform training on the entire dataset, challenging the assumption that more data always leads to better performance.
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.
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.
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.
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.
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.
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.
© 2024 by Linnk AI