Backpropagation-based visualizations have been proposed to interpret
convolutional neural networks (CNNs), however a theory is missing to justify
their behaviors: Guided backpropagation (GBP) and deconvolutional network
(DeconvNet) generate more human-interpretable but less class-sensitive
visualizations than saliency map. Motivated by this, we develop a theoretical
explanation revealing that GBP and DeconvNet are essentially doing (partial)
image recovery which is unrelated to the network decisions. Specifically, our
analysis shows that the backward ReLU introduced by GBP and DeconvNet, and the
local connections in CNNs are the two main causes of compelling visualizations.
Extensive experiments are provided that support the theoretical analysis.

该研究解释了反向传播可视化方法（如 GBP 和 DeconvNet）的行为与特点，提出它们实质上是对图像的重建而不是与网络决策相关的，这种重建与网络的反向传播中采用的 ReLU 函数和局部连接有关。

基於反向傳播可視化的令人困惑行為的理論解釋

A Theoretical Explanation for Perplexing Behaviors of  Backpropagation-based Visualizations

In this paper, we tackle the problem of RGB-D semantic segmentation of indoor
images. We take advantage of deconvolutional networks which can predict
pixel-wise class labels, and develop a new structure for deconvolution of
multiple modalities. We propose a novel feature transformation network to
bridge the convolutional networks and deconvolutional networks. In the feature
transformation network, we correlate the two modalities by discovering common
features between them, as well as characterize each modality by discovering
modality specific features. With the common features, we not only closely
correlate the two modalities, but also allow them to borrow features from each
other to enhance the representation of shared information. With specific
features, we capture the visual patterns that are only visible in one modality.
The proposed network achieves competitive segmentation accuracy on NYU depth
dataset V1 and V2.

本文在室内图像的 RGB-D 语义分割问题中，通过利用可以预测像素级类别标签的反卷积网络，开发了一种反卷积多模态新结构并提出了一种新的特征转换网络。该特征转换网络通过在两个模态之间发现共同特征并表征每种特性的方式将两种模态相关联。新网络在 NYU 深度数据集 V1 和 V2 上取得了有竞争力的分割精度。