Understanding CNNs from excitations
CoRR(2022)
摘要
Saliency maps have proven to be a highly efficacious approach for explicating
the decisions of Convolutional Neural Networks. However, extant methodologies
predominantly rely on gradients, which constrain their ability to explicate
complex models. Furthermore, such approaches are not fully adept at leveraging
negative gradient information to improve interpretive veracity. In this study,
we present a novel concept, termed positive and negative excitation, which
enables the direct extraction of positive and negative excitation for each
layer, thus enabling complete layer-by-layer information utilization sans
gradients. To organize these excitations into final saliency maps, we introduce
a double-chain backpropagation procedure. A comprehensive experimental
evaluation, encompassing both binary classification and multi-classification
tasks, was conducted to gauge the effectiveness of our proposed method.
Encouragingly, the results evince that our approach offers a significant
improvement over the state-of-the-art methods in terms of salient pixel
removal, minor pixel removal, and inconspicuous adversarial perturbation
generation guidance. Additionally, we verify the correlation between positive
and negative excitations.
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