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ASE 2020
Mon 21 - Fri 25 September 2020 Melbourne, Australia
Wed 23 Sep 2020 01:50 - 02:10 at Kangaroo - Software Engineering for AI (2) Chair(s): Aldeida Aleti

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.

Wed 23 Sep

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01:10 - 02:10
Software Engineering for AI (2) Research Papers / Industry Showcase at Kangaroo
Chair(s): Aldeida Aleti Monash University
Audee: Automated Testing for Deep Learning Frameworks
Research Papers
Qianyu Guo College of Intelligence and Computing, Tianjin University, Xiaofei Xie Nanyang Technological University, Yi Li Nanyang Technological University, Singapore, Xiaoyu Zhang Xi'an Jiaotong University, Yang Liu Nanyang Technological University, Singapore, Li Xiaohong TianJin University, Chao Shen Xi'an Jiaotong University
Towards Interpreting Recurrent Neural Networks through Probabilistic Abstraction
Research Papers
Guoliang Dong Computer College of Zhejiang University, Jingyi Wang Zhejiang University, Jun Sun Singapore Management University, Yang Zhang Zhejiang University, Xinyu Wang Zhejiang University, Dai Ting Huawei International Pte Ltd, Jin Song Dong National University of Singapore, Xingen Wang Zhejiang University
Towards Building Robust DNN Applications: An Industrial Case Study of Evolutionary Data Augmentation
Industry Showcase
Haruki Yokoyama Fujitsu Laboratories Ltd., Satoshi Onoue Fujitsu Ltd., Shinji Kikuchi Fujitsu Laboratories Ltd.