Search Results for author:
Found 6 papers, 2 papers with code
D3AD: Dynamic Denoising Diffusion Probabilistic Model for Anomaly Detection
Diffusion models have found valuable applications in anomaly detection by capturing the nominal data distribution and identifying anomalies via reconstruction.
Ranked #2 on
Anomaly Detection
on BTAD
(Segmentation AUPRO metric)
Anomaly Detection with Conditioned Denoising Diffusion Models
In this paper, we introduce Denoising Diffusion Anomaly Detection (DDAD), a novel denoising process for image reconstruction conditioned on a target image.
Ranked #2 on
Anomaly Detection
on MVTec AD
FullFormer: Generating Shapes Inside Shapes
Implicit generative models have been widely employed to model 3D data and have recently proven to be successful in encoding and generating high-quality 3D shapes.
Explaining Deep Neural Networks for Point Clouds using Gradient-based Visualisations
In recent years, several approaches have attempted to provide visual explanations of decisions made by neural networks designed for structured 2D image input data.
An Architectural Design for Measurement Uncertainty Evaluation in Cyber-Physical Systems
The mathematical description of the metrological uncertainty of fused or propagated values can be seen as a first step towards the development of a harmonized approach for uncertainty in distributed CPSs in the context of Industrie 4. 0.
CLAD: A Complex and Long Activities Dataset with Rich Crowdsourced Annotations
This paper introduces a novel activity dataset which exhibits real-life and diverse scenarios of complex, temporally-extended human activities and actions.
