Current status, application, and challenges of the interpretability of generative adversarial network models

Abstract

The generative adversarial network (GAN) is one of the most promising methods in the field of unsupervised learning. Model developers, users, and other interested people are highly concerned about the GAN mechanism where the generative model and the discriminative model learn from each other in a gameplay manner, which generates a causal relationship among output features, internal network structure, feature extraction process, and output results. Through the study of the interpretability of GANs, the validity, reliability, and robustness of the application of GANs can be verified, and the weaknesses of the GANs in specific applications can be diagnosed, which can provide support for designing better network structures. It can also improve security and reduce the decision‐making and prediction risks brought by GANs. In this article, the study of the interpretability of GANs is explored, and ways of the evaluation of the application effect of GAN interpretability techniques are analyzed. Besides, the effect of interpretable GANs in fields such as medical treatment and military is discussed, and current limitations and future challenges are demonstrated.

An Incremental Mutual Information-Selection Technique for Early Ransomware Detection

“…Furthermore, Ref. [55] discussed the interpretability issues of GAN models, stressing the necessity for feature-selection techniques that are capable of adapting to the fluidity of malware features. Collectively, these studies draw attention to the limitations of standard feature-selection approaches when combined with GAN frameworks, accentuating the demand for more flexible and evolving feature-selection methods in the realm of malware detection.…”