Automated Testing of Graphics Units by Deep-Learning Detection of Visual Anomalies

Lev Faivishevsky, Adi Szeskin, Ashwin K. Muppalla, Ravid Shwartz-Ziv, Itamar Ben Ari, Ronen Laperdon, Benjamin Melloul, Tahi Hollander, Tom Hope, Amitai Armon

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

We present a novel system for performing real-time detection of diverse visual corruptions in videos, for validating the quality of graphics units in our company. The system is used for several types of content, including movies and 3D graphics, with strict constraints on low false alert rates and real-time processing of millions of video frames per day. These constraints required novel solutions involving both hardware and software, including new supervised and weakly-supervised methods we developed. Our deployed system has enabled a 20X reduction of human effort and discovering new corruptions missed by humans and existing approaches.

Original languageAmerican English
Title of host publicationKDD 2021 - Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
PublisherAssociation for Computing Machinery
Pages2811-2821
Number of pages11
ISBN (Electronic)9781450383325
DOIs
StatePublished - 14 Aug 2021
Externally publishedYes
Event27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD 2021 - Virtual, Online, Singapore
Duration: 14 Aug 202118 Aug 2021

Publication series

NameProceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

Conference

Conference27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD 2021
Country/TerritorySingapore
CityVirtual, Online
Period14/08/2118/08/21

Bibliographical note

Publisher Copyright:
© 2021 ACM.

Keywords

  • anomaly detection
  • computer vision
  • deep learning
  • graphics processors validation
  • multiple instance learning

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