Importance-Driven Deep Learning System Testing

@article{Gerasimou2020ImportanceDrivenDL,
  title={Importance-Driven Deep Learning System Testing},
  author={Simos Gerasimou and Hasan Ferit Eniser and A. Sen and Alper Cakan},
  journal={2020 IEEE/ACM 42nd International Conference on Software Engineering: Companion Proceedings (ICSE-Companion)},
  year={2020},
  pages={322-323}
}
Deep Learning (DL) systems are key enablers for engineering intelligent applications. Nevertheless, using DL systems in safety- and security-critical applications requires to provide testing evidence for their dependable operation. We introduce DeepImportance, a systematic testing methodology accompanied by an Importance-Driven (IDC) test adequacy criterion for DL systems. Applying IDC enables to establish a layer-wise functional understanding of the importance of DL system components and use… Expand
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References

SHOWING 1-10 OF 13 REFERENCES
DeepGauge: Multi-Granularity Testing Criteria for Deep Learning Systems
  • L. Ma, Felix Juefei-Xu, +9 authors Yadong Wang
  • Computer Science, Mathematics
  • 2018 33rd IEEE/ACM International Conference on Automated Software Engineering (ASE)
  • 2018
Guiding Deep Learning System Testing Using Surprise Adequacy
  • Jinhan Kim, R. Feldt, S. Yoo
  • Computer Science
  • 2019 IEEE/ACM 41st International Conference on Software Engineering (ICSE)
  • 2019
DeepXplore: Automated Whitebox Testing of Deep Learning Systems
Explaining nonlinear classification decisions with deep Taylor decomposition
The Limitations of Deep Learning in Adversarial Settings
Explaining and Harnessing Adversarial Examples
Deep Learning
End to End Learning for Self-Driving Cars
...
1
2
...