GAN-Based Counterfactual Explanation on Images

Sammanfattning: Machine learning models are widely used in various industries. However, the black-box nature of the model limits users’ understanding and trust in its inner workings, and the interpretability of the model becomes critical. For example, when a person’s loan application is rejected, he may want to understand the reason for the rejection and seek to improve his personal information to increase his chances of approval. Counterfactual explanation is a method used to explain the different outcomes of a specific event or situation. It modifies or manipulates the original data to generate counterfactual instances to make the model make other decision results. This paper proposes a counterfactual explanation method based on Generative Adversarial Networks (GAN) and applies it to image recognition. Counterfactual explanation aims to make the model change the predictions by modifying the feature information of the input image. Traditional machine learning methods have apparent shortcomings in computational resources when training and have specific bottlenecks in practical applications. This article builds a counterfactual explanation model based on Deep Convolutional Generative Adversarial Network (DCGAN).The original random noise input of DCGAN is converted into an image, and the perturbation is generated by the generator in the GAN network, which is combined with the original image to generate counterfactual samples. The experimental results show that the counterfactual samples generated based on GAN are better than the traditional machine learning model regarding generation efficiency and accuracy, thus verifying the effectiveness and advancement of the method proposed in this article.