基于对比层级相关性传播的由粗到细的类激活映射算法研究

以卷积神经网络为代表的深度学习算法高度依赖于模型的非线性和调试技术，在实际应用过程中普遍存在黑箱属性，严重限制了其在安全敏感领域的进一步发展。为此，该文提出一种由粗到细的类激活映射算法(CF-CAM)，用于对深度神经网络的决策行为进行诊断。该算法重新建立了特征图和模型决策之间的关系，利用对比层级相关性传播理论获取特征图中每个位置对网络决策的贡献生成空间级的相关性掩码，找到影响模型决策的重要性区域，再与经过模糊化操作的输入图像进行线性加权重新输入到网络中得到特征图的目标分数，从空间域和通道域实现对深度神经网络进行由粗到细的解释。实验结果表明，相较于其他方法该文提出的CF-CAM在忠实度和定位性能上具有显著提升。此外，该文将CF-CAM作为一种数据增强策略应用于鸟类细粒度分类任务，对困难样本进行学习，可以有效提高网络识别的准确率，进一步验证了CF-CAM算法的有效性和优越性。

Study of Coarse-to-Fine Class Activation Mapping Algorithms Based on Contrastive Layer-wise Relevance Propagation

Deep learning algorithms represented by Convolutional Neural Networks (CNN) are highly dependent on the nonlinearity of the model and debugging techniques, which have generally black-box properties during practical applications, limiting severely their further development in security-sensitive fields. To this end, a Coarse-to-Fine Class Activation Mapping (CF-CAM) algorithm is proposed for diagnosing the decision-making behaviors of deep neural networks. The algorithm re-establishes the relationship between the feature map and the model decision, uses the contrastive layer-wise relevance propagation theory to obtain the contribution of each position in the feature map to the network decision, generates a spatial-level correlation mask and finds the important area that affects the model decision. After that, the mask is linearly weighted with the fuzzed input image and re-input into the network to obtain the target score of the feature map, and the deep neural network is explained from the coarse stage to the fine stage in the spatial domain and the channel domain. The experimental results show that the CF-CAM proposed in this paper has obvious advantages in terms of faithfulness and localization performance compared to other methods. In addition, this paper applies CF-CAM as a data enhancement strategy for the task of fine-grained classification of birds, which can effectively improve the accuracy of network recognition by learning difficult samples, further verify the effectiveness and superiority of this method.