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Active learning using weakly supervised signals for quality inspection
arXiv - CS - Logic in Computer Science Pub Date : 2021-04-07 , DOI: arxiv-2104.02973
Antoine Cordier, Deepan Das, Pierre Gutierrez

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.

中文翻译:

使用弱监督信号进行主动学习以进行质量检查

由于制造过程发展迅速,并且由于生产视觉方面每天都可能发生很大变化,因此快速更新基于机器视觉的检查系统的能力至关重要。不幸的是,卷积神经网络的监督学习需要大量带注释的图像,以便能够从新数据中有效学习。认识到来自生产线的大量连续生成的图像及其注释的成本,我们证明了可以对注释过程进行优先级排序和加速。在这项工作中,我们开发了一种方法,可以从快速挖掘的,弱(即部分)注释的数据中主动学习,从而使操作员能够在生产线上快速,直接地反馈信息,并解决机器视觉的一个重大缺陷:误报。我们还考虑了协变量偏移的问题,该问题不可避免地由于数据采集过程中条件的变化而产生。在这方面,我们表明领域对抗训练是解决此问题的有效方法。
更新日期:2021-04-08
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