DeepSaucer: Unified Environment for Verifying Deep Neural Networks
This provides a utility tool for researchers and practitioners in deep learning verification to streamline the process of applying multiple verification methods, though it is incremental as it builds on existing virtual environment technology.
The authors tackled the problem of time-consuming translations needed to apply multiple verification methods to deep neural networks by proposing DeepSaucer, a unified environment that retains and reuses code snippets for loading DNNs and running verification methods, reducing verification costs and demonstrating feasibility through implementation on Anaconda and use case examples.
In recent years, a number of methods for verifying DNNs have been developed. Because the approaches of the methods differ and have their own limitations, we think that a number of verification methods should be applied to a developed DNN. To apply a number of methods to the DNN, it is necessary to translate either the implementation of the DNN or the verification method so that one runs in the same environment as the other. Since those translations are time-consuming, a utility tool, named DeepSaucer, which helps to retain and reuse implementations of DNNs, verification methods, and their environments, is proposed. In DeepSaucer, code snippets of loading DNNs, running verification methods, and creating their environments are retained and reused as software assets in order to reduce cost of verifying DNNs. The feasibility of DeepSaucer is confirmed by implementing it on the basis of Anaconda, which provides virtual environment for loading a DNN and running a verification method. In addition, the effectiveness of DeepSaucer is demonstrated by usecase examples.