This paper proposes MOON, a white-box test input selection approach based on multi-objective optimization.
This paper proposes MOON, a white-box test input selection approach based on multi-objective optimization.
A neural network model is introduced to learn the nonlinear and time-varying dynamics of the controlled plant. The controller can be designed with no prior ...
Multi-Objective White-Box Test Input Selection for Deep Neural Network ...
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Abstract—To reveal incorrect behaviors and improve the qual- ity of DNN models through testing, a commonly used approach is to collect massive test inputs ...
The strategy of DeepGD is twofold: (1) trigger as many mispredicted inputs as possible by selecting inputs with high uncertainty scores, and (2) maximize the ...
The method was used to achieve the goal of judging common pneumonia and even COVID-19 more effectively. Where, the genetic algorithm was taken advantage to ...
[64] proposed MOON, a white- box test input selection method based on multi-objective optimization, which focuses on the coverage of neurons and uses the neuron ...
Multi-Objective White-Box Test Input Selection for Deep Neural Network Model Enhancement ... Objective Black-Box Test Selection Approach for Deep Neural Networks.
We present Adapt, a new white-box testing technique for deep neural networks. As deep neural networks are increasingly used in safety-first applications.
Missing: Objective Enhancement.
The results show that BET sig- nificantly outperforms existing white-box and black-box testing methods considering the effective error-inducing inputs found in.