Identifying Patterns for Convolutional Neural Networks in Regression Tasks to Make Specific Predictions via Genetic Algorithms

Abstract

Convolutional neural networks (CNNs) are effective tools for regression tasks. However, their black-box nature limits their applicability in high-impact and high-risk tasks. In this paper, a novel method is proposed to identify particular patterns in an image that can make the output of a CNN model equal to a specified value, thereby helping users understand the behaviours of CNNs. Specifically, in the proposed method, a set of binary filters is first randomly initialized. A genetic algorithm is then employed to evolve the binary filters such that the output of the CNN is equal to a specified value when taking a filtered image, which is obtained by convolving an original image and an evolved filter, as its input. Many experiments are conducted to evaluate the effectiveness of the proposed method. The results show that the proposed method is highly effective at identifying the patterns that can make a CNN output a specified value.