Abstract
Layer-wise relevance propagation is a framework which allows to decompose the prediction of a deep neural network computed over a sample, e.g. an image, down to relevance scores for the single input dimensions of the sample such as subpixels of an image. While this approach can be applied directly to generalized linear map**s, product type non-linearities are not covered. This paper proposes an approach to extend layer-wise relevance propagation to neural networks with local renormalization layers, which is a very common product-type non-linearity in convolutional neural networks. We evaluate the proposed method for local renormalization layers on the CIFAR-10, Imagenet and MIT Places datasets.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Bach, S., Binder, A., Montavon, G., Klauschen, F., Müller, K.-R., Samek, W.: On pixel-wise explanations for non-linear classifier decisions by layer-wise relevance propagation. PLOS ONE 10(7), e0130140 (2015)
Csurka, G., Dance, C.R., Fan, L., Willamowski, J., Bray, C.: Visual categorization with bags of keypoints. In: Workshop on Statistical Learning in Computer Vision, ECCV, pp. 1–22 (2004)
Krizhevsky, A.: Learning multiple layers of features from tiny images (2009). http://www.cs.toronto.edu/~kriz/cifar.html
Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: NIPS, pp. 1106–1114 (2012)
Lapuschkin, S., Binder, A., Montavon, G., Müller, K.-R., Samek, W.: Analyzing classifiers: fisher vectors and deep neural networks. In: Proceedings of IEEE CVPR, pp. 2912–2920 (2016)
Montavon, G., Bach, S., Binder, A., Samek, W., Müller, K.-R.: Explaining nonlinear classification decisions with deep taylor decomposition. CoRR, abs/1512.02479 (2015)
Russakovsky, O., Deng, J., Hao, S., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., Berg, A.C., Fei-Fei, L.: ImageNet large scale visual recognition challenge. IJCV 115, 1–42 (2015)
Samek, W., Binder, A., Montavon, G., Bach, S., Müller, K.-R.: Evaluating the visualization of what a deep neural network has learned. CoRR, abs/1509.06321 (2015)
Simonyan, K., Vedaldi, A., Zisserman, A.: Deep inside convolutional networks: visualising image classification models and saliency maps. CoRR, abs/1312.6034 (2013)
van de Sande, K.E.A., Gevers, T., Snoek, C.G.M.: Evaluating color descriptors for object and scene recognition. IEEE Trans. Pattern Anal. Mach. Intell. 32(9), 1582–1596 (2010)
Zeiler, M.D., Fergus, R.: Visualizing and understanding convolutional networks. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014, Part I. LNCS, vol. 8689, pp. 818–833. Springer, Heidelberg (2014)
Zhou, B., Lapedriza, A., **ao, J., Torralba, A., Oliva, A.: Learning deep features for scene recognition using places database. In: Advances in NIPS, pp. 487–495 (2014)
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this paper
Cite this paper
Binder, A., Montavon, G., Lapuschkin, S., Müller, KR., Samek, W. (2016). Layer-Wise Relevance Propagation for Neural Networks with Local Renormalization Layers. In: Villa, A., Masulli, P., Pons Rivero, A. (eds) Artificial Neural Networks and Machine Learning – ICANN 2016. ICANN 2016. Lecture Notes in Computer Science(), vol 9887. Springer, Cham. https://doi.org/10.1007/978-3-319-44781-0_8
Download citation
DOI: https://doi.org/10.1007/978-3-319-44781-0_8
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-44780-3
Online ISBN: 978-3-319-44781-0
eBook Packages: Computer ScienceComputer Science (R0)