English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

We present a deep learning system for testing graphics units by detectingnovel visual corruptions in videos. Unlike previous work in which manualtagging was required to collect labeled training data, our weak supervisionmethod is fully automatic and needs no human labelling. This is achieved byreproducing driver bugs that increase the probability of generatingcorruptions, and by making use of ideas and methods from the Multiple InstanceLearning (MIL) setting. In our experiments, we significantly outperformunsupervised methods such as GAN-based models and discover novel corruptionsundetected by baselines, while adhering to strict requirements on accuracy andefficiency of our real-time system.

A Weak Supervision Approach to Detecting Visual Anomalies for Automated Testing of Graphics Units