  A number of backpropagation-based approaches such as DeConvNets, vanilla
Gradient Visualization and Guided Backpropagation have been proposed to better
understand individual decisions of deep convolutional neural networks. The
saliency maps produced by them are proven to be non-discriminative. Recently,
the Layer-wise Relevance Propagation (LRP) approach was proposed to explain the
classification decisions of rectifier neural networks. In this work, we
evaluate the discriminativeness of the generated explanations and analyze the
theoretical foundation of LRP, i.e. Deep Taylor Decomposition. The experiments
and analysis conclude that the explanations generated by LRP are not
class-discriminative. Based on LRP, we propose Contrastive Layer-wise Relevance
Propagation (CLRP), which is capable of producing instance-specific,
class-discriminative, pixel-wise explanations. In the experiments, we use the
CLRP to explain the decisions and understand the difference between neurons in
individual classification decisions. We also evaluate the explanations
quantitatively with a Pointing Game and an ablation study. Both qualitative and
quantitative evaluations show that the CLRP generates better explanations than
the LRP. The code is available.
