Program slicing has been widely applied in a variety of software engineering tasks. However, existing program slicing techniques only deal with traditional programs that are constructed with instructions and variables, rather than neural networks that are composed of neurons and synapses.
In this paper, we introduce NNSlicer, the first approach for slicing deep neural networks based on data-flow analysis. Our method understands the reaction of each neuron to an input based on the difference between its behavior activated by the input and the average behavior over the whole dataset. Then we quantify the neuron contributions to the slicing criterion by recursively backtracking from the output neurons, and calculate the slice as the neurons and the synapses with larger contributions.
We demonstrate the usefulness and effectiveness of NNSlicer with three applications, including adversarial input detection, model pruning, and selective model protection. In all applications,
NNSlicer significantly outperforms other baselines that do not rely on data flow analysis.
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 | ||