Low-cost architecture performance evaluation strategy based on pixel difference degree contrast measurement

The time and effort required to manually design deep neural architectures is extremely high, which has led to the development of neural architecture search technology as an automatic architecture design method. However, the neural architecture search convergence process is slow and expensive, and the process requires training a large number of candidate architectures to get the final result. If the final accuracy of an architecture can be predicted from its initial state, this problem can be greatly alleviated. Therefore, this paper proposes a low-cost architecture performance evaluation strategy based on pixel difference degree contrast measurement, which takes 1) the difference matrix value between the feature map generated in the untrained architecture and the original image, and 2) the predicted accuracy of the neural network as evaluation indices. A new multi-index weight comprehensive measurement strategy was introduced to comprehensively score the multi-index, the real architecture performance can be approximately represented by score, which greatly reduces the cost of architecture evaluation. The experimental show that the proposed scoring strategy is highly correlated with real architecture accuracy. In the practical engineering application research, this strategy can search a high-performance architecture with an accuracy of 96.2% within 343.3 s, which proves that the proposed strategy can significantly improve the search efficiency in practical applications, reduce the subjectivity of artificial architecture design, and promote the application of practical time-consuming projects. •The implicit relationship between the initial feature map and the final accuracy of the model is proved.•Low-cost architecture performance evaluation strategy is proposed to reduce the cost of model evaluation.•The strategy shows excellent performance in practical application.