标题： 度量学习的相似特征激活的视觉解释 显示英文标题

标题： Visual Explanation via Similar Feature Activation for Metric Learning

摘要： 可视化解释图增强了深度学习模型决策的可信度，并为开发图像识别任务中的新算法提供了宝贵的指导。 基于softmax的卷积神经网络的类别激活图（CAM）及其变体（如Grad-CAM和Relevance-CAM）已被广泛用于探索其可解释性，这些方法需要一个全连接层作为分类器来进行决策。 然而，这些方法不能直接应用于度量学习模型，因为这类模型缺乏充当分类器的全连接层。 为了解决这一局限性，我们提出了一种新的可视化解释方法，称为相似特征激活图（SFAM）。 该方法通过引入通道贡献重要性评分（CIS），根据两张图像嵌入之间的相似性来衡量特征的重要性。 解释图通过线性组合所提出的权重与来自CNN模型的特征图构建而成。 定量和定性实验表明，SFAM为使用欧氏距离或余弦相似性作为相似性度量的CNN模型提供了非常有前景的可解释可视化解释。 显示英文摘要

摘要： Visual explanation maps enhance the trustworthiness of decisions made by deep learning models and offer valuable guidance for developing new algorithms in image recognition tasks. Class activation maps (CAM) and their variants (e.g., Grad-CAM and Relevance-CAM) have been extensively employed to explore the interpretability of softmax-based convolutional neural networks, which require a fully connected layer as the classifier for decision-making. However, these methods cannot be directly applied to metric learning models, as such models lack a fully connected layer functioning as a classifier. To address this limitation, we propose a novel visual explanation method termed Similar Feature Activation Map (SFAM). This method introduces the channel-wise contribution importance score (CIS) to measure feature importance, derived from the similarity measurement between two image embeddings. The explanation map is constructed by linearly combining the proposed importance weights with the feature map from a CNN model. Quantitative and qualitative experiments show that SFAM provides highly promising interpretable visual explanations for CNN models using Euclidean distance or cosine similarity as the similarity metric.