1 code implementation • 9 Jan 2024 • Justin Tebbe, Jawad Tayyub
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)
1 code implementation • 25 May 2023 • Arian Mousakhan, Thomas Brox, Jawad Tayyub
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
no code implementations • 20 Mar 2023 • Tejaswini Medi, Jawad Tayyub, Muhammad Sarmad, Frank Lindseth, Margret Keuper
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.
no code implementations • 26 Jul 2022 • Jawad Tayyub, Muhammad Sarmad, Nicolas Schönborn
In recent years, several approaches have attempted to provide visual explanations of decisions made by neural networks designed for structured 2D image input data.
no code implementations • 17 Aug 2020 • Wenzel Pilar von Pilchau, Varun Gowtham, Maximilian Gruber, Matthias Riedl, Nikolaos-Stefanos Koutrakis, Jawad Tayyub, Jörg Hähner, Sascha Eichstädt, Eckart Uhlmann, Julian Polte, Volker Frey, Alexander Willner
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.
no code implementations • 11 Sep 2017 • Jawad Tayyub, Majd Hawasly, David C. Hogg, Anthony G. Cohn
This paper introduces a novel activity dataset which exhibits real-life and diverse scenarios of complex, temporally-extended human activities and actions.