Survey of Interpretation Methods for Deep Neural Networks
摘要
Deep Neural Networks(DNN) are featured by non-linear non-convex properties,multiple hidden layers,feature vectorization,massive model parameters,etc.However,its weak interpretability has strangled their theory development and practical applications,so the interpretation methods for DNN have attracted attention from artificial intelligence researchers.In view of the strong requirements for the interpretability of DNN in high-risk decision-making fields such as military,finance,medicine,and transportation,this paper comprehensively combs and analyses typical network interpretation methods for typical networks such as Convolutional Neural Networks(CNN),Recurrent Neural Networks(RNN),and Generative Adversarial Networks(GAN).It also summarizes and compares existing interpretation methods.Then,based on the current development trend of DNN,the future research directions of interpretation methods are prospected.