Excitement Surfeited Turns to Errors: Deep Learning Testing Framework Based on Excitable Neurons. (arXiv:2202.07464v2 [cs.LG] UPDATED)

Despite impressive capabilities and outstanding performance, deep neural
networks (DNNs) have captured increasing public concern about their security
problems, due to their frequently occurred erroneous behaviors. Therefore, it
is necessary to conduct a systematical testing for DNNs before they are
deployed to real-world applications. Existing testing methods have provided
fine-grained metrics based on neuron coverage and proposed various approaches
to improve such metrics. However, it has been gradually realized that a higher
neuron coverage does \textit{not} necessarily represent better capabilities …

deep learning framework testing testing framework