Manuscript received January 22, 2024; revised February 17, 2024; accepted March 15, 2024; published August 6, 2024

Abstract—Examining and potentially adjusting one’s cognitive processes in response to dissatisfaction with one’s performance is a fundamental aspect of intelligence. Remarkably, such sophisticated abstract concepts necessary for achieving Artificial General Intelligence can be effectively incorporated into basic Machine Learning algorithms. In this study, we introduce a method for replicating self-awareness through a supervisory Artificial Neural Network (ANN), which monitors patterns in the activation functions of an underlying ANN to identify signs of substantial uncertainty within the underlying ANN and, consequently, the reliability of its predictions. The underlying ANN in this context is a Convolutional Neural Network (CNN) ensemble primarily utilized for tasks related to facial recognition and facial expression analysis. We evaluate the performance of the supervisory ANNs using various activation functions as they learn to gauge the dependability of predictions made by the Inception v3 CNN ensemble. To conduct computational experiments, we employ a facial data set that incorporates makeup and occlusion factors. These experiments are designed to mimic real-world conditions where the training data set exclusively consists of images without makeup or occlusion, while the test data set comprises images featuring makeup and occlusion. This partitioning ensures the model is tested under challenging out-of-training data distribution scenarios.

Keywords—meta-learning, trustworthiness, uncertainty estimation, face recognition, occlusions

Cite: Stanislav Selitskiy and Natalya Selitskaya, "Activation Functions Study for the Trustworthiness Supervisor Artificial Neural Networks," Journal of Image and Graphics, Vol. 12, No. 3, pp. 269-275, 2024.

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