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- 저자 : Lapuschkin, Sebastian (검색결과 2 건)
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Explaining nonlinear classification decisions with deep Taylor decomposition
Montavon, Gré,goire,Lapuschkin, Sebastian,Binder, Alexander,Samek, Wojciech,Mü,ller, Klaus-Robert Elsevier 2017 Pattern recognition Vol.65 No.-
<P><B>Abstract</B></P> <P>Nonlinear methods such as Deep Neural Networks (DNNs) are the gold standard for various challenging machine learning problems such as image recognition. Although these methods perform impressively well, they have a significant disadvantage, the lack of transparency, limiting the interpretability of the solution and thus the scope of application in practice. Especially DNNs act as black boxes due to their multilayer nonlinear structure. In this paper we introduce a novel methodology for interpreting generic multilayer neural networks by decomposing the network classification decision into contributions of its input elements. Although our focus is on image classification, the method is applicable to a broad set of input data, learning tasks and network architectures. Our method called deep Taylor decomposition efficiently utilizes the structure of the network by backpropagating the explanations from the output to the input layer. We evaluate the proposed method empirically on the MNIST and ILSVRC data sets.</P> <P><B>Highlights</B></P> <P> <UL> <LI> A novel method to explain nonlinear classification decisions in terms of input variables is introduced. </LI> <LI> The method is based on Taylor expansions and decomposes the output of a deep neural network in terms of input variables. </LI> <LI> The resulting deep Taylor decomposition can be applied directly to existing neural networks without retraining. </LI> <LI> The method is tested on two large-scale neural networks for image classification: BVLC CaffeNet and GoogleNet. </LI> </UL> </P>
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Evaluating the Visualization of What a Deep Neural Network Has Learned
Samek, Wojciech,Binder, Alexander,Montavon, Gregoire,Lapuschkin, Sebastian,Muller, Klaus-Robert IEEE 2017 IEEE transactions on neural networks and learning Vol.28 No.11
<P>Deep neural networks (DNNs) have demonstrated impressive performance in complex machine learning tasks such as image classification or speech recognition. However, due to their multilayer nonlinear structure, they are not transparent, i.e., it is hard to grasp what makes them arrive at a particular classification or recognition decision, given a new unseen data sample. Recently, several approaches have been proposed enabling one to understand and interpret the reasoning embodied in a DNN for a single test image. These methods quantify the 'importance' of individual pixels with respect to the classification decision and allow a visualization in terms of a heatmap in pixel/input space. While the usefulness of heatmaps can be judged subjectively by a human, an objective quality measure is missing. In this paper, we present a general methodology based on region perturbation for evaluating ordered collections of pixels such as heatmaps. We compare heatmaps computed by three different methods on the SUN397, ILSVRC2012, and MIT Places data sets. Our main result is that the recently proposed layer-wise relevance propagation algorithm qualitatively and quantitatively provides a better explanation of what made a DNN arrive at a particular classification decision than the sensitivity-based approach or the deconvolution method. We provide theoretical arguments to explain this result and discuss its practical implications. Finally, we investigate the use of heatmaps for unsupervised assessment of the neural network performance.</P>
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