  Because manufacturing processes evolve fast, and since production visual
aspect can vary significantly on a daily basis, the ability to rapidly update
machine vision based inspection systems is paramount. Unfortunately, supervised
learning of convolutional neural networks requires a significant amount of
annotated images for being able to learn effectively from new data.
Acknowledging the abundance of continuously generated images coming from the
production line and the cost of their annotation, we demonstrate it is possible
to prioritize and accelerate the annotation process. In this work, we develop a
methodology for learning actively, from rapidly mined, weakly (i.e. partially)
annotated data, enabling a fast, direct feedback from the operators on the
production line and tackling a big machine vision weakness: false positives. We
also consider the problem of covariate shift, which arises inevitably due to
changing conditions during data acquisition. In that regard, we show
domain-adversarial training to be an efficient way to address this issue.
