Analysis of the effectiveness and robustness of neural networks with early exits in computer vision tasks

Many embedded systems and Internet of Things (IoT) devices use neural network algorithms for various information processing tasks. At the same time, developers face the problem of insufficient computing resources for effective functioning, especially in real-time (pseudo-) tasks. In this regard, the urgent task is to find a balance between the quality of the results and computational complexity. One of the ways to increase the computational efficiency of neural networks is to use neural network architectures with early exits (for example, BranchyNet), which allow making decisions before passing through all layers of the neural network, depending on the source data for a given reliability of the results. The purpose of the study: to analyze the applicability, effectiveness and robustness of neural networks with early exits (BranchyResNet18) in computer vision tasks. The analysis is based on the GTSRB road sign dataset. The research methodology is an experimental efficiency analysis based on the calculation of the number of floating-point operations (FLOP) to obtain results with a given accuracy, and an experimental robustness analysis based on the generation of various noise effects and adversarial attacks. Research results: estimates of the effectiveness of neural networks with early exit and their robustness to unintended and intentional disturbances have been obtained.