mlsanityData to 14 Oct 2026

Layer-wise Relevance Propagation for Neural Networks with Local Renormalization Layers

arXiv 1604.00825 ↗First posted 4 Apr 2016cs.CVcited by 199

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Abstract (arXiv)

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 mappings, 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.

Key facts. Cited by 199 papers in the map (data through 14 Oct 2026). First posted on arXiv 4 Apr 2016.

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Citing papers per year: 2021: 23; 2022: 41; 2023: 36; 2024: 42; 2025: 34; 2026: 23.

199 papers.

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