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ICSE 2020
Wed 24 June - Thu 16 July 2020
Sat 11 Jul 2020 00:00 - 00:12 at Goguryeo - P26-Deep Learning Testing and Debugging Chair(s): Tim Menzies

As deep neural networks are increasingly being deployed in practice, their efficiency has become an important issue. While there are compression techniques for reducing the network’s size, energy consumption and computational requirement, they only demonstrate empirically that there is no loss of accuracy, but lack formal guarantees of the compressed network, e.g., in the presence of adversarial examples. Existing verification techniques such as ReluVal and DeepPoly provide formal guarantees but they are designed for analyzing a single network instead of the relationship between two networks. To fill the gap, we develop a new method for differential verification of two closely related networks. Our method consists of a fast but approximate forward interval analysis pass and a backward pass that iteratively refines the approximation. There are two main innovations. During the forward pass, we exploit structural and behavioral similarities of the two networks to more accurately compute the symbolic ranges of all neurons. In the backward pass, we leverage the gradient differences to more accurately compute the refinement. Our experiments show that, compared to state-of-theart verification tools, our method can achieve orders-of-magnitude speedup and prove many more properties than existing tools.

Sat 11 Jul

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00:00 - 01:00
P26-Deep Learning Testing and DebuggingTechnical Papers at Goguryeo
Chair(s): Tim Menzies North Carolina State University
00:00
12m
Talk
ReluDiff: Differential Verification of Deep Neural NetworksArtifact ReusableTechnical
Technical Papers
Brandon Paulsen University of Southern California, Jingbo Wang University of Southern California, Chao Wang USC
Pre-print
00:12
12m
Talk
Structure-Invariant Testing for Machine TranslationTechnical
Technical Papers
Pinjia He ETH Zurich, Clara Meister ETH Zurich, Zhendong Su ETH Zurich, Switzerland
00:24
12m
Talk
Automatic Testing and Improvement of Machine TranslationTechnical
Technical Papers
Zeyu Sun Peking University, Jie M. Zhang University College London, UK, Mark Harman Facebook and University College London, Mike Papadakis University of Luxembourg, Lu Zhang Peking University, China
00:36
12m
Talk
Testing DNN Image Classifier for Confusion & Bias ErrorsArtifact ReusableTechnicalArtifact Available
Technical Papers
Yuchi Tian Columbia University, Ziyuan Zhong Columbia University, Vicente Ordonez University of Virginia, Gail Kaiser Columbia University, Baishakhi Ray Columbia University, New York
00:48
12m
Talk
Repairing Deep Neural Networks: Fix Patterns and ChallengesArtifact ReusableTechnicalArtifact Available
Technical Papers
Md Johirul Islam Iowa State University, Rangeet Pan Iowa State University, USA, Giang Nguyen Dept. of Computer Science, Iowa State University, Hridesh Rajan Iowa State University, USA