Is Neuron Coverage a Meaningful Measure for Testing Deep Neural Networks?
Recent effort to test deep learning systems has produced an intuitive and compelling test criterion called neuron coverage (NC), which resembles the notion of traditional code coverage. NC measures the proportion of neurons activated in a neural network and it is implicitly assumed that increasing NC improves the quality of a test suite. In an attempt to automatically generate a test suite that increases NC, we design a novel diversity promoting regularizer that can be plugged into existing adversarial attack algorithms. We then assess whether such attempts to increase NC could generate a test suite that (1) detects adversarial attacks successfully, (2) produces natural inputs, and (3) is unbiased to particular class predictions. Contrary to expectation, our extensive evaluation finds that increasing NC actually makes it harder to generate an effective test suite: higher neuron coverage leads to fewer defects detected, less natural inputs, and more biased prediction preferences. Our results invoke skepticism that increasing neuron coverage may not be a meaningful objective for generating tests for deep neural networks and call for a new test generation technique that considers defect detection, naturalness, and output impartiality in tandem.
Tue 10 NovDisplayed time zone: (UTC) Coordinated Universal Time change
01:30 - 02:00 | |||
01:30 2mTalk | Correlations between Deep Neural Network Model Coverage Criteria and Model Quality Research Papers Shenao Yan Rutgers University, USA, Guanhong Tao Purdue University, USA, Xuwei Liu Purdue University, USA, Juan Zhai Rutgers University, USA, Shiqing Ma Rutgers University, USA, Lei Xu Nanjing University, China, Xiangyu Zhang Purdue University DOI | ||
01:33 1mTalk | Deep Learning Library Testing via Effective Model GenerationACM SIGSOFT Distinguished Paper Award Research Papers Zan Wang Tianjin University, China, Ming Yan Tianjin University, China, Junjie Chen Tianjin University, China, Shuang Liu Tianjin University, China, Dongdi Zhang Tianjin University, China DOI | ||
01:35 1mTalk | Detecting Numerical Bugs in Neural Network ArchitecturesACM SIGSOFT Distinguished Paper Award Research Papers Yuhao Zhang Peking University, Luyao Ren Peking University, China, Liqian Chen National University of Defense Technology, China, Yingfei Xiong Peking University, Shing-Chi Cheung Hong Kong University of Science and Technology, China, Tao Xie Peking University DOI | ||
01:37 1mTalk | Dynamic Slicing for Deep Neural Networks Research Papers Ziqi Zhang Peking University, China, Yuanchun Li Microsoft Research, China, Yao Guo Peking University, Xiangqun Chen Peking University, Yunxin Liu Microsoft Research, China DOI | ||
01:39 1mTalk | Grammar Based Directed Testing of Machine Learning Systems Journal First Sakshi Udeshi Singapore University of Technology and Design, Sudipta Chattopadhyay Singapore University of Technology and Design | ||
01:41 1mTalk | Is Neuron Coverage a Meaningful Measure for Testing Deep Neural Networks? Research Papers Fabrice Harel-Canada University of California at Los Angeles, USA, Lingxiao Wang University of California at Los Angeles, USA, Muhammad Ali Gulzar University of California at Los Angeles, USA, Quanquan Gu University of California at Los Angeles, USA, Miryung Kim University of California at Los Angeles, USA DOI | ||
01:43 1mTalk | Operational Calibration: Debugging Confidence Errors for DNNs in the Field Research Papers Zenan Li Nanjing University, China, Xiaoxing Ma Nanjing University, China, Chang Xu Nanjing University, China, Jingwei Xu Nanjing University, China, Chun Cao Nanjing University, China, Jian Lu Nanjing University, China DOI | ||
01:45 15mTalk | Conversations on ML Testing 1 Research Papers Fabrice Harel-Canada University of California at Los Angeles, USA, Ming Yan Tianjin University, China, Sakshi Udeshi Singapore University of Technology and Design, Shenao Yan Rutgers University, USA, Yuhao Zhang Peking University, Zenan Li Nanjing University, China, Ziqi Zhang Peking University, China, M: Hamid Bagheri University of Nebraska-Lincoln, USA | ||