一句话：研究工业 GUI 数据集上数据增强对 DNN 图像分类与目标检测鲁棒性的影响，并总结实践启示。
数据增强通过加入逼真变换扩充训练数据，已被证明能提升公开数据集上图像分类模型的鲁棒性，但其对工业数据集是否有效尚不清楚。本文以工业内部图形用户界面（GUI）数据集为对象，在图像分类和目标检测任务上评估多种数据增强技术，并访谈开发者以提炼实践经验。结果表明，基于遗传算法的数据增强在图像分类模型鲁棒性上优于两种随机方法。作者进一步总结出两条经验：数据增强技术应保持训练速度以避免拖慢开发，并应具备面向多种任务的可扩展性。
Data augmentation techniques that increase the amount of training data by adding realistic transformations are used in machine learning to improve the level of accuracy. Recent studies have demonstrated that data augmentation techniques improve the robustness of image classification models with open datasets; however, it has yet to be investigated whether these techniques are effective for industrial datasets. In this study, we investigate the feasibility of data augmentation techniques for industrial use. We evaluate data augmentation techniques in image classification and object detection tasks using an industrial in-house graphical user interface dataset. As the results indicate, the genetic algorithm-based data augmentation technique outperforms two random-based methods in terms of the robustness of the image classification model. In addition, through this evaluation and interviews with the developers, we learned following two lessons: data augmentation techniques should (1) maintain the training speed to avoid slowing the development and (2) include extensibility for a variety of tasks.
DOI 原文 ·
@inproceedings{paperbot5899,
title = {Towards building robust DNN applications},
author = {Haruki Yokoyama and Satoshi Onoue and Shinji Kikuchi},
booktitle = {ASE},
year = {2020},
doi = {10.1145/3324884.3421841}
}