  We present a deep learning system for testing graphics units by detecting
novel visual corruptions in videos. Unlike previous work in which manual
tagging was required to collect labeled training data, our weak supervision
method is fully automatic and needs no human labelling. This is achieved by
reproducing driver bugs that increase the probability of generating
corruptions, and by making use of ideas and methods from the Multiple Instance
Learning (MIL) setting. In our experiments, we significantly outperform
unsupervised methods such as GAN-based models and discover novel corruptions
undetected by baselines, while adhering to strict requirements on accuracy and
efficiency of our real-time system.
