基于可解释基拆解和知识图谱的深度神经网络可视化

Deep Neural Network Visualization Based on Interpretable Basis Decomposition and Knowledge Graph

近年来,以卷积神经网络(CNN)等为代表的深度学习模型,以其深度分层学习,无标签化学习等优势,已在图像识别为代表的各个领域得到日益广泛的应用.然而,深度神经网络模型由于其内在的黑盒原理,对其内部工作机制的解释仍然面临巨大挑战,其可解释性问题已成为了研究界和工业界的前沿性热点研究课题.针对现有研究存在的缺乏基于图谱的可解释性方法的问题,以及可解释基模型的图谱构建优势,本文提出了一种基于可解释基拆解和知识图谱的深度神经网络可视化方法.首先采用一种面向可解释基模型特征拆解结构的知识图谱构建方法,构建了场景和解释特征之间的解释关系和并列关系等图谱信息;利用场景-特征的解释关系网络,提出了一种基于Jaccard系数的场景间相似度聚类方法;针对现有可解释基模型对相似的场景,其解释特征重合率可能很高的问题,提出了一种基于场景的判别性特征提取方法,在特征拆解结果中能对每一类样本分别提取出能够区别此类和其他类并且拥有同等重要性的拆解特征(即判别性特征);针对现有可解释基的深度网络可视化测试缺乏保真度测试的问题,提出了一种适于深度神经网络的保真度测试方法.保真度测试和人类置信度测试,均表明本文所提方法可取得优异效果.

Recently,owing to the advantages of deep-layered learning and unlabeled learning,etc.,deep learning models represented by convolutional neural network,deep neural network,recurrent neural network,have gained increasing applications in various fields,such as image recognition,video,and natural language processing.To achieve the high transparency and security assurance of deep learning models,the interpretability research of deep neural networks is of great theoretical significance and industrial application value and recently gains increasingly attention.However,because of the intrinsic black-box characteristics of the deep learning models,the interpretation of its internal structure and the running mechanism is still of great challenges,including the rigorous theoretical results originated from the manual observations of large-scale training and testing set,and scarce appropriate explanation of the learning results based on the human understanding.Moreover,most of the existing researches analyzing the decision-making process of deep learning models only from a local perspective and lacks a graphical representation based on the overall understanding.On the other hand,the interpretable basis decomposition(IBD)model has the advantages that its interpretation result is not only a strict corresponding relation from scene to feature,but also is a kind of semi-structured data which can facilitate IBD based knowledge map construction from it.Aiming at the problem that existing deep neural network visualization researches lacks the interpretability based on the knowledge map and the well-suited knowledge map representability of IBD,we propose a deep neural network visualization approach based on interpretable basis decomposition and knowledge map,which fully takes the advantage of map construction ability of interpretable basis decomposition.Firstly,we propose a knowledge map construction method based on the feature decomposition structure of IBD,which constructs the map information,such as the interpretation relationship and juxtaposition relationship,between the scene and the interpretable feature.Then,a similarity clustering algorithm between scenes using Jaccard coefficient based on the interpretation relation network of scenes and features is proposed.Based on a scene discriminant feature extraction method,the decomposed features that can distinguish this class from other classes are extracted from each type of sample,namely discriminant features.Meanwhile,we quantify the accuracy of discriminant feature extraction by means of manual evaluation by exploring the difference between different models’understanding of the recognition target and that of human beings.Furthermore,a fidelity test method for deep network has been proposed to solve the problem that existing research lacks fidelity test.We combine the multi-feature thermal spectrograms into a comprehensive characteristic thermal spectrogram,and then use the Hadmag product to refuse the comprehensive characteristic thermal spectrogram with the original image to obtain the characteristic fusion spectrogram.The luminance labeled depth neural network classification model of feature fusion map was used to identify the target location pixel area,and the target location ability of thermal spectrum map was measured by comparing the deviation of input original map and feature fusion map to the model classification ability,so as to obtain the fidelity of the interpretable basis decomposition model.Both the fidelity test and the human confidence test show that the proposed method can achieve excellent results.