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
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.
