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ICSE 2021
Mon 17 May - Sat 5 June 2021

With machine learning models especially Deep Neural Network (DNN) models becoming an integral part of the new intelligent software, new tools to support their engineering process are in high demand. Existing DNN debugging tools are either post-training which wastes a lot of time training a buggy model and requires expertises, or limited on collecting training logs without analyzing the problem not even fixing them. In this paper, we propose AUTOTRAINER, a DNN training monitoring and automatic repairing tool which supports detecting and auto repairing five commonly seen training problems. During training, it periodically checks the training status and detects potential problems. Once a problem is found, AUTOTRAINER tries to fix it by using built-in state-of-the-art solutions. It supports various model structures and input data types, such as Convolutional Neural Networks (CNNs) for image and Recurrent Neural Networks (RNNs) for texts. Our evaluation on 6 datasets, 495 models show that AUTOTRAINER can effectively detect all potential problems with 100% detection rate and no false positives. Among all models with problems, it can fix 97.33% of them, increasing the accuracy by 47.08% on average

Conference Day
Tue 25 May

Displayed time zone: Amsterdam, Berlin, Bern, Rome, Stockholm, Vienna change

10:30 - 11:30
1.1.3. Deep Neural Networks: Validation #1Technical Track at Blended Sessions Room 3 +12h
Chair(s): Oscar DiesteUniversidad Politécnica de Madrid
10:30
20m
Paper
Operation is the hardest teacher: estimating DNN accuracy looking for mispredictionsTechnical Track
Technical Track
Antonio GuerrieroUniversità di Napoli Federico II, Roberto PietrantuonoUniversità di Napoli Federico II, Stefano RussoUniversità di Napoli Federico II
Pre-print Media Attached
10:50
20m
Paper
AUTOTRAINER: An Automatic DNN Training Problem Detection and Repair SystemTechnical Track
Technical Track
Xiaoyu ZhangXi'an Jiaotong University, Juan ZhaiRutgers University, Shiqing MaRutgers University, Chao ShenXi'an Jiaotong University
Pre-print Media Attached
11:10
20m
Paper
Self-Checking Deep Neural Networks in DeploymentTechnical Track
Technical Track
Yan XiaoNational University of Singapore, Ivan BeschastnikhUniversity of British Columbia, David RosenblumGeorge Mason University, Changsheng SunNational University of Singapore, Sebastian ElbaumUniversity of Virginia, Yun LinNational University of Singapore, Jin Song DongNational University of Singapore
Pre-print Media Attached
22:30 - 23:30
1.1.3. Deep Neural Networks: Validation #1Technical Track at Blended Sessions Room 3
22:30
20m
Paper
Operation is the hardest teacher: estimating DNN accuracy looking for mispredictionsTechnical Track
Technical Track
Antonio GuerrieroUniversità di Napoli Federico II, Roberto PietrantuonoUniversità di Napoli Federico II, Stefano RussoUniversità di Napoli Federico II
Pre-print Media Attached
22:50
20m
Paper
AUTOTRAINER: An Automatic DNN Training Problem Detection and Repair SystemTechnical Track
Technical Track
Xiaoyu ZhangXi'an Jiaotong University, Juan ZhaiRutgers University, Shiqing MaRutgers University, Chao ShenXi'an Jiaotong University
Pre-print Media Attached
23:10
20m
Paper
Self-Checking Deep Neural Networks in DeploymentTechnical Track
Technical Track
Yan XiaoNational University of Singapore, Ivan BeschastnikhUniversity of British Columbia, David RosenblumGeorge Mason University, Changsheng SunNational University of Singapore, Sebastian ElbaumUniversity of Virginia, Yun LinNational University of Singapore, Jin Song DongNational University of Singapore
Pre-print Media Attached